{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>000700</td>\n",
       "      <td>36047000700</td>\n",
       "      <td>7</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>176774</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON ((-74.00154 40.69279, -74.00132 40.693...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>000900</td>\n",
       "      <td>36047000900</td>\n",
       "      <td>9</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>163469</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>333</td>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>POLYGON ((-73.99405 40.69090, -73.99374 40.691...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>001100</td>\n",
       "      <td>36047001100</td>\n",
       "      <td>11</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>168507</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>POLYGON ((-73.99073 40.69305, -73.99045 40.693...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>001300</td>\n",
       "      <td>36047001300</td>\n",
       "      <td>13</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>293167</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>419</td>\n",
       "      <td>398</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>POLYGON ((-73.99141 40.69863, -73.99131 40.699...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>002000</td>\n",
       "      <td>36047002000</td>\n",
       "      <td>20</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>154138</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>POLYGON ((-74.01867 40.64741, -74.01809 40.647...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        36        047    000700  36047000700      7  Census Tract   G5020   \n",
       "1        36        047    000900  36047000900      9  Census Tract   G5020   \n",
       "2        36        047    001100  36047001100     11  Census Tract   G5020   \n",
       "3        36        047    001300  36047001300     13  Census Tract   G5020   \n",
       "4        36        047    002000  36047002000     20  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20  ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S   176774         0  ...        0        0        0        0   \n",
       "1          S   163469         0  ...        0        0        0        0   \n",
       "2          S   168507         0  ...        0        0        0        0   \n",
       "3          S   293167         0  ...        0        0        0        0   \n",
       "4          S   154138         0  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0       58       49        0        9   \n",
       "1        0      333      306        0       27   \n",
       "2        0       23        0        0       23   \n",
       "3        0      419      398        0       21   \n",
       "4        0      134        0        0      134   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-74.00154 40.69279, -74.00132 40.693...  \n",
       "1  POLYGON ((-73.99405 40.69090, -73.99374 40.691...  \n",
       "2  POLYGON ((-73.99073 40.69305, -73.99045 40.693...  \n",
       "3  POLYGON ((-73.99141 40.69863, -73.99131 40.699...  \n",
       "4  POLYGON ((-74.01867 40.64741, -74.01809 40.647...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/ny_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd938fd1-bf94-4c8a-bb4b-2b902e65e6a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 5411 popn tracts for NYCLI\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"NYCLI\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population NYCLI voter-age population\n",
      "state pop= 20201249 VAP pct Hispanic is  0.18186607708102898\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP NYCLI\n",
      "total state pop= 20201249 , VAP pct Black is  0.14463236416153893\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for NYCLI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for NYCLI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "668 2117 0.017599203489999885 nonPolygon tract no, pop, area\n",
      "1.772029100001415e-05\n",
      "0.01758148319899987\n",
      "1655 2382 0.007483559311000001 nonPolygon tract no, pop, area\n",
      "0.005469334573999986\n",
      "8.5032115000003e-06\n",
      "3.12293099999656e-06\n",
      "0.0008542772130000359\n",
      "3.4989656999994264e-05\n",
      "3.922327800000866e-05\n",
      "0.0010741084464999792\n",
      "1656 1633 0.013914949535000085 nonPolygon tract no, pop, area\n",
      "0.0007168596070000174\n",
      "1.0955643999991814e-05\n",
      "0.00018290268900003233\n",
      "0.01227629610750006\n",
      "0.0007279354874999836\n",
      "1658 2351 0.0184604741209998 nonPolygon tract no, pop, area\n",
      "9.311173349998023e-05\n",
      "0.018367362387499822\n",
      "1898 1675 0.0003607014640000017 nonPolygon tract no, pop, area\n",
      "1.8426150000023423e-07\n",
      "0.0003605172025000015\n",
      "2678 0 8.138234999995787e-06 nonPolygon tract no, pop, area\n",
      "2.019172000000323e-06\n",
      "6.119062999995463e-06\n",
      "2860 2476 0.013573784286500107 nonPolygon tract no, pop, area\n",
      "0.005434501027000043\n",
      "0.008139283259500063\n",
      "3032 2947 0.0006053421529999931 nonPolygon tract no, pop, area\n",
      "8.725684600001756e-05\n",
      "0.0005180853069999756\n",
      "3501 3985 0.014661514379499889 nonPolygon tract no, pop, area\n",
      "1.140133199998478e-05\n",
      "0.014650113047499904\n",
      "4949 498 0.0017131703064997526 nonPolygon tract no, pop, area\n",
      "0.0006981804384998428\n",
      "0.0010149898679999098\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69edb6b2-7b88-43ca-8017-ff4d271ce3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For NY, we'll convex-hull these scattered minor areas\n",
    "convexList = [1656, 1658, 1655, 668, 4949, 3032, 1898, 3501, 2860, 2678]\n",
    "for c in range(len(convexList)):\n",
    "    m = convexList[c]\n",
    "    plotPoly(tractGeom[m])\n",
    "    tractGeom[m] = tractGeom[m].convex_hull\n",
    "    plotPoly(tractGeom[m])\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    plt.text(x+0.03,y+0.03,m,fontsize=9)\n",
    "    notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREIPIE</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 12-9</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-8218479.283 5265873.297, -8218067.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-1</td>\n",
       "      <td>57</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-8216488.778 5256926.035, -8216484.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-2</td>\n",
       "      <td>248</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-8214177.005 5257088.554, -8214188.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-3</td>\n",
       "      <td>368</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-8214253.148 5257026.663, -8214220.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-4</td>\n",
       "      <td>349</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-8212448.658 5257024.999, -8212504.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTY     PRECINCT  G20PREDBID  G20PRERTRU  G20PRELJOR  \\\n",
       "0      36      001  Albany  Albany 12-9          30          26           1   \n",
       "1      36      001  Albany   Albany 1-1          57          20           1   \n",
       "2      36      001  Albany   Albany 1-2         248          38           2   \n",
       "3      36      001  Albany   Albany 1-3         368          71           7   \n",
       "4      36      001  Albany   Albany 1-4         349          60           3   \n",
       "\n",
       "   G20PREGHAW  G20PREIPIE  G20PREOWRI  \\\n",
       "0           0           0           0   \n",
       "1           0           0           0   \n",
       "2           3           0           1   \n",
       "3           7           0           0   \n",
       "4           2           3           0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-8218479.283 5265873.297, -8218067.0...  \n",
       "1  POLYGON ((-8216488.778 5256926.035, -8216484.4...  \n",
       "2  POLYGON ((-8214177.005 5257088.554, -8214188.6...  \n",
       "3  POLYGON ((-8214253.148 5257026.663, -8214220.3...  \n",
       "4  POLYGON ((-8212448.658 5257024.999, -8212504.0...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/ny_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREIPIE</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 12-9</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-73.82786 42.69639, -73.82415 42.694...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-1</td>\n",
       "      <td>57</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-73.80997 42.63729, -73.80994 42.637...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-2</td>\n",
       "      <td>248</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-73.78921 42.63837, -73.78931 42.638...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-3</td>\n",
       "      <td>368</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-73.78989 42.63796, -73.78960 42.638...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-4</td>\n",
       "      <td>349</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-73.77368 42.63795, -73.77418 42.636...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTY     PRECINCT  G20PREDBID  G20PRERTRU  G20PRELJOR  \\\n",
       "0      36      001  Albany  Albany 12-9          30          26           1   \n",
       "1      36      001  Albany   Albany 1-1          57          20           1   \n",
       "2      36      001  Albany   Albany 1-2         248          38           2   \n",
       "3      36      001  Albany   Albany 1-3         368          71           7   \n",
       "4      36      001  Albany   Albany 1-4         349          60           3   \n",
       "\n",
       "   G20PREGHAW  G20PREIPIE  G20PREOWRI  \\\n",
       "0           0           0           0   \n",
       "1           0           0           0   \n",
       "2           3           0           1   \n",
       "3           7           0           0   \n",
       "4           2           3           0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-73.82786 42.69639, -73.82415 42.694...  \n",
       "1  POLYGON ((-73.80997 42.63729, -73.80994 42.637...  \n",
       "2  POLYGON ((-73.78921 42.63837, -73.78931 42.638...  \n",
       "3  POLYGON ((-73.78989 42.63796, -73.78960 42.638...  \n",
       "4  POLYGON ((-73.77368 42.63795, -73.77418 42.636...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15376 0.3827282310466085 8496883 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see NJ code for convex-hulling tracts\n",
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eb241d56-c85d-46c9-9e7e-1deb5b86cb5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downstate Pop =  12429981.0 which is  15.998  districts' worth\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ID tracts that are in DOWNstate NY\n",
    "nDistricts = 26\n",
    "isDownstateTract = [0] * nTracts\n",
    "isDownstatePrecinct = [0] * nPrecincts\n",
    "avgDistrictPop = np.sum(tractPop)/nDistricts\n",
    "#let's visualize north of NYC\n",
    "sumDownstatePop = 0.\n",
    "xCut = -74.\n",
    "yCut = 41.01\n",
    "slope = 0.3\n",
    "pt1 = Point(xCut - 0.5,yCut - 0.5*slope)\n",
    "pt2 = Point(xCut + 0.8, yCut + 0.8*slope)\n",
    "cutLine = LineString([pt1,pt2])\n",
    "x,y = cutLine.xy\n",
    "plt.plot(x,y)\n",
    "for m in range(nTracts):\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    if (y - yCut) < slope * (x - xCut) :\n",
    "        sumDownstatePop += tractPop[m]\n",
    "        isDownstateTract[m] = 1\n",
    "        if (y - yCut ) > -5 + slope * (x - xCut):  #for zooming in on White Plains, change \"5\" to 0.2\n",
    "            plotPoly(tractGeom[m])\n",
    "print(\"downstate Pop = \",sumDownstatePop,\"which is \",round(sumDownstatePop/avgDistrictPop,4),\" districts' worth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac461040-0ec3-4da6-90c1-6ec1c5072cfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downstate Voters =  4799665.0 which is about 14.6867  districts worth\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now ID the DOWNstate VTDs\n",
    "sumDownstateVoters = 0.   #not needed, just curious here\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if (y - yCut) < slope * (x - xCut) :\n",
    "        sumDownstateVoters += vtdTrump[p] + vtdBiden[p]\n",
    "        isDownstatePrecinct[p] = 1\n",
    "        if (y - yCut ) > -5 + slope * (x - xCut):  #for zooming in on White Plains, change \"5\" to 0.2\n",
    "            plotPoly(vtdGeom[p])\n",
    "print(\"downstate Voters = \",sumDownstateVoters,\"which is about\",round(nDistricts*sumDownstateVoters/\n",
    "      (np.sum(vtdTrump)+np.sum(vtdBiden)), 4),\" districts worth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "faa842d7-ba32-41f4-8218-865b15427ff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38, 68, 133, 561, 634, 712, 777, 846, 861, 875, 950, 1191, 1242, 1243, 1269, 1319, 1410, 1411, 1417, 1419, 1429, 1431, 1481, 1529, 1561, 1580, 1691, 1705, 1729, 1730, 1961, 2025, 2027, 2259, 2280, 2317, 2319, 2338, 2383, 2409, 2439, 2441, 2444, 2470, 2471, 2495, 2508, 2678, 2681, 2782, 3089, 3108, 3142, 3273, 3288, 3290, 3292, 3294, 3296, 3297, 3302, 3303, 3304, 3305, 3308, 3309, 3312, 3325, 3417, 3448, 3459, 3515, 3546, 3550, 3554, 3556, 3573, 3603, 3646, 3680, 3713, 3821, 3822, 3826, 3977, 3994, 4101, 4165, 4173, 4174, 4232, 4492, 4624, 4706, 4768, 4781, 5024, 5281, 5301, 5307, 5352, 5369, 5396]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag coastal low-population tracts.\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 50\n",
    "for m in range(nTracts):\n",
    "    if isDownstateTract[m] == 1 and tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        if tractGeom[m].area > 0.003:  #only show tract numbers for larger coastal tracts\n",
    "            plt.text(x, y-0.05,m, fontsize=9)\n",
    "       \n",
    "        if y< 42 : #only worry about Atlantic ocean tracts\n",
    "            if lakeTracts == [-99999]:\n",
    "                lakeTracts = [m]\n",
    "                # isSkippedTract[m] = 1   #not yet\n",
    "            else:\n",
    "                if m==99999:   #no NYC or LI interior empty tracts\n",
    "                    thisTract = \"interior\"\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    # isSkippedTract[m] = 1   #notyet\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "06852664-90ce-4851-a459-246f97ee245c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Flag these bigger few coastal tracts\n",
    "oceanTracts = [1243,1319,1691,5024,133,2782]\n",
    "for m in oceanTracts:\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    plotPoly(tractGeom[m])\n",
    "    plt.text(x, y,m, fontsize=9)\n",
    "    isSkippedTract[m]=1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df63628e-0ef6-45db-9ab8-e90844f95c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zoom in on NE Long Island\n",
    "pt1 = Point(-72.3,40.8)\n",
    "pt2 = Point(-71.75,40.9)\n",
    "pt3 = Point(-71.9,41.3)\n",
    "pt4 = Point(-72.45,41.2)\n",
    "cutLine = LineString([pt2,pt3])\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "plotPoly(clipPoly)\n",
    "for m in range(nTracts):\n",
    "    if isDownstateTract[m] == 1 and tractPop[m] > 1: #minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if tractGeom[m].distance(clipPoly) < 0.1:\n",
    "            plotPoly(tractGeom[m])\n",
    "            plt.text(x,y,m,fontsize=6)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dca817fc-8a1a-4037-bb00-3f0e2e871491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  19 tracts w pop 50731.0  to reassign to tracts...\n",
      "[2876, 2925, 2927, 5215]\n",
      "I have flagged  41 precincts to reassign to precincts...\n",
      "[13773, 13774, 13775, 13780, 13789, 13830, 13831, 13835, 13838]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.05: #and tractGeom[t].centroid.x > -76.4 and tractGeom[t].centroid.y > 38.4:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.05: # and vtdGeom[p].centroid.x > -76.4 and vtdGeom[p].centroid.y > 38.4:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0JSMjU4ss9DKnFZcoUVnjIKnQjFaloEu4d71dydKKLTz2+36em9yBjqHezetAEOCa392Lr8VJ0PWqFhy9jMyFgSz0Mo1SXm1Hq1Ki1zRt67sqq4Nl8QX8uSebramluMRj60C9o31pG+SJBJitTv7el1t77Z6f9rD6oWHNH6BC6Z7Vtx7R/LoyMhcBstDL1KGoysaO9FJSCs04XCKrDhcSn1uJp07FZ9f2ZGBsQIP1JElid2Y5P2/PZOH+PGocLiL89NwyqBUmTy2tAz1ILbIwd0s6qUUWahwuqu2u2vo3DWzFzYNbnanblJG5qJC9bmTIKa9hzeFC5u3OZndmeZ1r3SN9GNrWxF97c0krtjCxSwj3jIilXZAnkgRpJRZWJxTy264sEgvMGDRKJncN5creEXSP8Glw8/iUIjN3zN1FSpGZB0e3ZdawWBQKOahJRuZUOJHXjSz0FynFZhtrDhfy554cNqeUANDaZOTynuH0j/GnfYgXADq121xTbXfy2doUvt6YhsXuQqdWYHOK/PPn0yXcm6v7RHJJ11A8tA2/KFodLp788wDzdufgZ9TwwVXdGdSm4TcEGRmZ5iG7V8rUYc3hQh76bR+lFjvhvnoeHtOWcZ2CaW3yaHAGDmDQqHhwTDtuHNiKhftzSS+pxqhREuytZ3CbACL8Gs/d/sbSw8zbncNVvSO4a3hsk+rIyMicOrLQX+A4XSJKhYAgCFgdLl5bnMCcLRnEBXvy5fW96B7h0yyzia9Rw3X9o5s9jvjcCuZsTufafpG8PKVzs+vLyMicPLLQX8DM3ZrBiwviifA1MKlrKIv255JSZOHmQa14ZGy7WrPM6Sa92MKtc3biZ9Ty8Jh2Z6RPGRmZY8hCf4GyObmYFxfE0zbIkxqHiw9WJRETYGTOTX0Y2tZ0xsaRWmTm6i+3YneKfH9LX3wMmjPWt4yMjBtZ6C9AdmWUcePsHbQKMPLDLX3x1quxu0Q0SsVxbfCnZxylzPx2Bxqlgp9v60+7YM8z1reMjMwxZKE/R3DaXZTlV5MRX4Kl3IZWr8LgrcE3xEhFQTXVVQ7aDwjB00933DZEl8icnw7x0b5MbEqJK3tFsDy+gHBfPZ3CvdGqzoypBtzRrjfN3om/UcN3N/Ul0l9eeJWROVvIQn8OYDU7+PW1HVSVWAFQaRQ47fU31DiwNpvuYyKJ6uiPb7ABhdK9b0x1pZ20fUXsX5NNda6Fm9Dxp9HGy4sS6tQf2zGI96/qfkZs828vP4IoSsy9ua/sXSMjc5aRhf4cYMfiNKpKrQy/No6IDn54+GgRRYnK4hrMZTZ8gw04bC5WfnuILfNS2DIvBUEh4OGrRXRJWMptAHgFHJvtXxrsz/gbOqBVKUgpMrMusYhvN6Xz0K/7+PiaHqf9nnallzG6Y5As8jIy5wCy0J8DFKQmojfa6DAotPacUiHgG2zEN9hYe27aE70xl1nJPlJGeUE1lcVWlGoFXv46KotryIgvBQHa9ApizM0da+tF+BkY1i4QlULgyw1ptFmZyH0j25w2e32J2UZhlZVIWeRlZM4JZKE/y1QWF5Kx5zMA9q0Kp+vIvics7+GrI65fSO1xVamVJZ8doCizitA2Pky8swtBrbwarPv4+PaUWhy8tzKJ9GILd49oQ2uTsUUFP6+ihptn70QQBMZ3Cmm8goyMzGlHFvqzSHFWBn+89lzt8frvv6HLiD5NFl671cl3T24GYNztnYjpZjphXaVC4K0ruhDhp+ej1cnM35tLqLeOQW0CGBEXxMj2gaiVJ7dfvNXhYs3hQp75K54au5Ovb+gle9nIyJwjyEJ/lnA5nSx6/00kl4trX3+fXYu3kLD+Z+a9/jmXP3FHk9pI21cMQIeBIbTuHtikOgqFwP2j2nJFz3DWJRaxMamYpQfz+XVnNq1NRp6Z1IFh7U7cVmGVlTmb0zmSX0Woj57ccisbk4uwOkRaBRj56da+tAmSRV5G5lxBFvqzxNY/fqI4K4NLH36aoFatGXt7FPnJiaTvXciOhZ3oPWnQCetbym1s+CWR4Bgvhl4T1+z+w30NXNM3imv6RuF0iaxMKOT1JQnM/HYHI+ICeWZSB1oFGOvUcYkS32/N4O1lR6h2uIgJMLIttRQvvZrpvSIY2T6IfjH+aFQn91YgIyNzepCzV55hyvJz2fjjHBK3baLjsFGMu/P+2mtWSzWf3nYzWmMgs754/7ht2K1OFn28n8L0SqY/3QefoJZZ9LQ5XczelM4Hq5Kwu0Q+u7YnI9sHAbA3q5yn5x/gYE4lg2IDePHSjsSYPFqk3wsNSRRJ2LiW6G49MXg1c8csGZmTRM5eeQ4gSRKJWzex/PP3kUSJ/lfMoO9lV9YpozMa0BpMWM2FZB/OJDwusl479honf723h6LMKkbO7NBiIg+gVSkxalU4RQm9WomvUUNFtYM3lx3mx+2ZmDy0fDSjOxM7h5zRCNvzjarSYpZ8/A4Alz/5ItFdT787q4zMiZCF/gxQnJjB2t++ImP/HkzRMVz60JN4BwbXK1dVWolG70NNZSq/PH8f98z+GY1OW3tdEiVWzj5EUZaZcbd1JqZ7y+as+W1nFk/PP8jQtiau6xfFH7uy+XtvLha7kxsHtOKB0W3w1KlbtM8LEa+AQExRrSjKSOOPV5+l0/AxjLntbgSFbNKSOTvIQn+acORbKPszGXtGJQDKconhM2+n25gJKJR1I1PLC/KxWczMf/tTzCVHAIgbOLWOyAMc3ppH2r5iBk1r06Iib3W4eHPpEb7ZlAZAUkEVt3y3E51awYTOIdwyKIYOoQ27bMo0zLWvv8c3999ORUE+B9csR6lSMvLmWfKbkMxZQRb6FsZltlOxNJ3qXQUIKgWqKA+cGWZ6+4zFapZQKJUUHUihcG0C5DnxvyyObev+IHnHljrt9L9iXL22cxPL0eiUdBkR3mLjPZxfyf0/7+VwflXtudaBHtwzsg0TOofgrZdn8CeDQqGk09BRbPr1e7qOmci+5YvQe/kw8MprzvbQZC5CZKFvQUr37aFg5ToMZXF4D2yL55AwlF5azIWlHH53HUV7q6nevQQ/yQNv3It0xX8l0HfmtHpC/+39N+IfOYDrX38chVJB/IYcErcXEBTj1SKzQlGU+GZTWp18OAEeWt64vHPtAqzMqaH3cr8F9Zx4KS6Hna1//ER1eRkDr7pOXqSVOaPIQt9CiKKT+IK7sHcvwKu8H1GTfqC63My2n9ewN+kAFVoLAFpJxQR7D/wlT1ySE/xVBMe25aFfFjL3sfsoTE+pbbMkcyvrfz6EQqXhwJpsIjv6M+aWjscbQpNxukTGvreelCJL7bnLe4Tz5IQ4/D20J6gp0xwKUpPReXjiExTC6FvvRqFQsn/VUvavWsq0Z14lslOXsz1EmYsEWehbiNy8X7GrCgCwGA6xdfFDZFp2cCRpMIFSEL1juxLcOow/ly1goWYXIx2daXdDL6LaHTPDjJt1P989ek/tscbrBuI3FALQdVQEA6bGNmvbv+Nhc4pU1DgBeOWyTkzqEiqbaE4DJdlZ+IaGIQgCglLJ6NvuxuDjy9Y/fuK3l54ktG174gYOoePQkWj0cl4gmdOH7EffAjidZjZvGYHDUVLvWllpO6ZOXYjiqMdFaXYR337xNQ7BxaX6HrR/fHyd8v+bPgmA6S+8gd4ritzEcgIiPAlp7Y0kSWxJKaHa7iI6wEBMgMdJC7/N6TrjG5FcTBRlpPHdo/cw6Krr67nROqxW9i5fRMKmdRSlp6JQut/qwuI6oNHpMUW1olW3nvUW7WVkToTsR3+aSUl5G4ejhC4dvmT/oVsBiPN7m+SKTfj6/cm2bR/Sv/99APiFm5gyeQpzF/5MVlU5EWmFeLRypxwoSE0GoNvYSYTHuU00/qHuoCSHS+Sm2TvYkFRc22/HUC+eu6QjfVr5NXvMZ3ITkouNqtJiVn71CWqdnq6jJ9S7rtbp6D35cnpPvpzcxARSdm4j69ABdi6YhyS69yEw+vjSbsAQ4gYOIbh1W/mBLHNKNFnoBUFQAjuBHEmSJgmCMA14HmgP9JEkqcEpuCAI3wCTgEJJkjqd+pDPLTIzvyY7Zy4RETdiCh6BKW084ZHX4hfWj2DXJJYt30BNzW/AfbV1Ynq2w7TIm4PKTMJ+2k7HJydRUVjAn2++iIevH/2mTq/Xz5+7c9iQVMxj4+LoF+NHfG4lH69J5srPtzCpSwhPTGhPmI/+DN65zL+xVVvIij9AXvIR9q9YgtNuZ+yd96HzOHH0cGjb9oS2bQ+A6HLhcjlJ37uLQ+tXs2/5InYv/guf4BDaDxpO9/GXoPeQcwjJNJ8mm24EQXgQ6AV4HRX69oAIfA48fAKhHwKYge+aKvTni+kmv2AB8fH3E2gaT6dO7+N+FtZl1er7kaQF9O+3A0lykJ29jYLC7eTlbcFgKMe7uCutu9zB8rlfU11ZzlXPv0FAZHSdNuxOkeFvryXAQ8P8uwbWzu5q7C4+W5fCZ+tSEAS4fUhr7hzW+ozsICXjxlZdzYaf5nBg1TJElxNBoSAkth1j77wfv9CwU2rbajGTtH0zhzeuJTP+ADqDkW7jLsHTP4CyvBwUCgWtevQmrF0HecYvc0LTTZOEXhCEcGAO8ArwoCRJk/51bS0nEPqjZaKBhReK0EuSi8zMr0lJfQdv7+506zobpbJhb5Vdu2ZTXvESDocWtdpWe95u90CjMQNQkxpOylp/Ln/qRcLb1/+IXll0iC83pDH7xt4NZpbMKa/htcUJLNyfh7dezb7nxrTQncqcCIfNyu8vP0Ne0hE6jxxD+0HDCIqJRa09/r6+J0tRRhrrf/iW9H27AVCq1UiiiOhyYfTxZfJDT9a+GchcnLSEjf494FHgtL03CoJwG3AbQGRk/Rwv5wpOZxXxhx6muHglpoDRtG//5nFFHqB9+8ksWDAHUVQTHNwDP7/OREYOwtc3Cqe9hnVrB6CNzGH8va/WEXlJksitsLIiPp8vN7gjVoe2bTgaNsxHz4dXd2fh/jwqahwte8Myx2XH3/PITTrMJfc/Rtt+J842eqqYolpx+ZMvYjWbqamqwCcoBIfdRvKOraz48iN2L/5bFnqZ49Ko0AuC8I99fZcgCMNO10AkSfoC+ALcM/rT1c/JYDanUFS0GLMliZKStbhc1bRt+xzhYdc1+sqsVLqYOPEztNpA1Grf2vOSJHIk6WlQVdIq7DFi2w2svfbtpjQ+XpNCsdn9BtA1woeHxzS+IOelUzEirml56WVOnbzkI3gFmGjTd2DjhVsInYdHrd1fo9MT0aEzRh9fyvJyz9gYZM4/mjKjHwhMFgRhAqADvARB+F6SpGtP79DOHi5JwiqKFNmdvJ10mN9LJLpIRp7T7SPQNJaIiJl4etYPXBJFG4WFSzGZxpKZ+SX5BQuprk6uva7RBBAWejURETeSlv4R+QXziWn1AK1a3VanndeXHMZTp+LFSzvSO9qP9iGN55kRBIHBbU1sSS3B4RJPeqcomabTtu9Aln/+ASu//Jig1rFUFhWiUCpRKJQoVCo0egMqjYaq4iKcdhsGb18M3t4ERrfGPzzilPq2lJexevYXJG3dhITEhHsebqG7krkQaVToJUl6AngC4OiM/uELVeQlSWJBUQUvpuSQba1rAtkv9KBjzyvx0R7fiyIz8xtSUt+uPfbx7k1wq/sxGFphsxVSVraFtPQPycz6GpermvDw64mOvqteOx1CvUjMr0IQBNo1Y6emy7qFsWh/Hl+sT+Wu4bFNridzcnQcNpLS3Gx2LpgHq0AQFEiSWL+gIKBQKBBdrtpTQTGxhMV1JKZ7byI6dUahaPoCem5iAn++/gK26mp6TppCtzETGsyGKiPzD80KmPqX0E8SBOEy4EPABJQDeyVJGisIQijwlSRJE47W+QkYBgQABcBzkiR9faJ+zvRirFOU2Flp4fXUPLZWWOhg1DElyBdvlZJhfp5szd3CfZk+TDSk8XXfy+rVlySJvXtnUlq2sfZcVNSdxLauP8uqqkogNe09tNog2rV9HkFwz7ytTiuV9koCDYHklNfw+B/72ZBUzEuXduS6/tFNvpe7ftzNsoP5jO4QxLB2Joa1CyTIq+UXB2WOUZSRhiRJmKJagSQhiiIupwOH1YrDasXo44tKq8VmsWApLyN9327i16+iKD0VgIDIaIJatabXJVMJiIg6YV8Fqcn89MzDePqbuPSRpxstL3PxcMpeN2eakxX6EruTTeVm8mx2fFQq/NRK/NUqBEHgiKWGRIsNuyTSSq/F4hLZXmEh22on22rH7BLxUyt5IiaEGSH+KAWBVdk7uS3JTjU6JBR0VGaxasgl9fqVJJHVa9rUHvv69KNr129OuEgLYHPZ+N/O/7EjfwfJ5W4Tz/hW43lj8BsAdH9pBRM7h/DKZZ0bvfcXFsSzYF8eo9oHklJkJr2kmqIqt40/LtiTYe0CGd7ORO9ovxZJoyBzakiSRE5CPMXZmSRsXEtxZjpOu50B02bQZ8q0467HrP3uK/YuW8gtH32Dh2/zA+VkTp4acxUqjQa15tzMB3XRRMZeuieJ5Grbca9rFQIqQcDicr9etzFoiTXoGODjQV8fI8P9vPD6V8To4vxMLMQAMMMznTe6T2ywXUFQMGTwLtZv6AlAZdVBioqWEhx86XHHIkoiL255kb9T/qZvSN9aoV+StoS2iptYcqCU8moHKw4VNEnot6aWUmy28fOOLAA8dcd+tYfzq0guNPPZuhR6RPrw8pTOcn75s4wgCIR36ER4h050GzOB6soKVn/zGRt//g6dh0eDEbUAVaUleAUGyyJ/hqiurODgmhUkbFhDcVYGGr2Byx5/rjZy/XzhghL6V9qEM32fO/ujn1rJ3ZFBtDXqcIgibY06ovVaFECR3YlaIeCrPvHtv9VjCn1TV/F6tsiPVdH8uD4egKnGVD7pMxWXy4rNVoBeH4la7cOQwbspLFxCXv6fxB96EFG0ERp6Zb12naKTZzc9y4LUBczqNos7u97JN3t/5d19LwHw6oqNxPm1Y1zHYLpH+jTp3mNMRhLyKrljaGvaBXuwLbWUrLJqnC6JHeml/HhrP9KKzby59AiXfLSRyV1Dub5/FF56NU6XhFMUCfPR42PQNP0Dl2kxDF7eTLz3Eaorytn403e06z+kwaha0emEc/At/EKjLC+H3UsWcHDNCpx2G6Ft2zPo6hvYt2Ixa+d8xbWvvXu2h9gsLijTDbg9Zn7JL+WttHzybA5G+3vxVOsQ4ownnx7A5rQxYONacqRjedrzh3dj955rKStz55GPiryNiIgb0WoDEUUb+/ffQUnpBjp2fJfgoLrmnvd2vcfXB7/mnu73cGvnWxEEgSqrlSt/f5ws+yYElyf7b1nVrGjHihoHl3y4EUGApfcNQa9xv5lUVDsY/r+1RPgZmD9rABkl1Vzz1TZyymvqtaFUCPSI9CHC14DJS0tRpY2M0mp6Rflybb8oIvzkDIunm4LUZH546kG6jBzHqFtm1bu++bcf2fLHT9z9zS9oDfLv41RwOR1Yysowl5ViKS+lsqiQ3KQj5CYmYC4pRqFU0X7wMHpfMhX/cHdsz55lC1n9zWeMv/shOgwefpbvoC4XjY3+31S7RL7OLuKV1DwEYGmvtnT1PPl/DJvTTtSGQwBs7KIh1r8DRxJfJDt7Tp1y0dF3E2gaR1LSy5SVbwVg2NADKJXH+h73xzhifWL5aORH9fq5/a/32Vz+FXNH/0m30OZ5zmxJKeHqL7dy86BWPDOpQ+35L9an8Oriw+x4ahRfbUjl8/Wpdep9PKMHSgUcyKlgS0oJBZU2Cqus+Bk1hPno2ZddgV6t5I87B9AuWM61crpZ/NH/SN6+hWteexf/sLpumFnx+/n1xSeZcO8jtB849CyN8PzCYbeRn5zonlAVF1FemE92Qjy5hw/hdNjrlPUMMNXmH2rbdwAefv51rrucTn576SlyDsfTulc/pjzy9EmNyW6tQaXWtGiG0ovGRv9vDEoF90QF0c/Hg0t2J/FdTjH/izv5iFuLs7r25937bqDEIxiL5UjtOaXSA5fLTHr6J6SnHxNwjSagjshX2ivJNedyaeuG7fdXdRrF5o1f8eKGr3hu8F10DW26v3X/1v5c3z+KbzalMb5TML2i3Xbcf/zwd2eW1c7kn5nUgSP5lfy6M5sSi43r+kUxrlNIbVuSJNW+UWSWVHPFZ5u596c9LHtgSJPHI3NyDLrqOtL37WbBO69x7evvo1If2ysgvH0nvExBHFi1TBb6E1BVaiU/pYKcpHL2Ln4Dl6OwznX/8Ei6jBqHf0QUHr5+GH398PTzx+Dtc8J2lSoVU594ng9vmEbKzq388dpztB80DP+wCLwCg0CSyDy4n5Rd2yjNyUZQCER06Ex0154Et45FozeQtmcn815/HoDhM2+j88ixp32B94IV+n/o7W3kxrAA5uQUc3O4iQ4ezTfhHKyq5so9qYCCq6Xv8KcUjbo9AVF3EGgaWyd4ymYrIjv7O9IzPgEgJGRa7bUaZw2vbXsNCYnhkQ2/9g1t1R7N2tYkWRdxzfIlqFwhSDhQCTqWXvkTJg+3aFeWF+O01WAvSkWh1qJQ6RA0Om7toqFi20Z2fPMrgfd9SqS/gf4x/gR76Xhp4SEqahx0CPHiun5RuESJrNIanv0rnsUH8rh3ZBv6tfJHoRDqmI0i/Q2M7RjMgv1y9OWZwCsgkHGz7ufP118gYcMaOo84lrtIUCjoPm4S6+Z+TdreXbTq1vMsjvTcQRQlyvIslOSayTlcxuEt+YiihOiqqBX5y594AU9TIF4BplPKR6TR6bln9q9s/+sPDq5ZTvreXfXK6Dy9CG7dBqfNxq5Ff7Hj7z8A8DIFYTUf2595zewv2Pbnr7TpM4AB02Y0+qA5WS5Y082/KXM46b81gW6eBn7u1rpZddOqbYzeeQSFAPcG1TDc+RvRUbMwGmPqlU0qS2Jz7mZ6BPags6lz7aw4uyqbuYfmsiB1AVX2KmZ1ncWd3e48bp81DhsrUnYye99fFNtysLlqqFYk8UKvL5jasT/bf3+HPgdfaHTs1tgJ6K74DHTe/LU3h/t+3gvAgrsH0TncvWepKEr8sjOLVxcnUGV10reVH2G+epbHF2B3iSDBHcNa8+3GNNoGe/LHnQOa9fnJnBySJPHxTVcRN3AIo26pG1TncjqY/dAsFAol17/1EUrVBT9fq4etxsnBddkk7SxEdIpYym3Yre6ANIVKoMPAUMoqE0hZ8z1IVoz+3bnjk5dafByiy0VJThbl+blUFLp3mAuOaUNou/a1Zhl7TTVZhw5SlJFGcVYGTrud1j370GnYKLIPx7N78d+k7dmBWm/gkvsfP+ktJi9K082/8VWreCA6iOeSc1ldUskI/6a7Fv6YV4LZJbK4Rxt6eBuBvg2W25y7mdtX3F57PD56PG1827A5dzM7C9wPrd7Bvbmn+z10D+zeYBuiKJJ6cBMHfvwI0+oDPCIpqLl9KgXto3g1+T32755P0JE9tDv4PgDbYh/A4dsaSZLAaUNw2VCIdny8fYktXYfu8Hz4qA8Mf4JLvSPQD3OSXy3QSVcIlRZQG1Bovbi6TySXdgvl7WWJfLMpDW2WgindwiirtrP8UAGfrU1BQuK96d2a/LnJnBqCIOAbGkZZfl69a+X5+ah1eorSU0nft4vWPRv+m7xQcdhc/PrKdiqLrQTHeGMMNBDW1pfgGC8CIjwR9XZmz16EcsdKkKyMvfc5Og3sfVrGolAqMUVGY/pPavF/o9EbaN2zD6179ql3LaJDZyI6dKYoM51fnnuMrfN+Pi17CV8UQg9wY1gA3+YU80JKLoN8PdAojp8LZnFROR9lFnJtiD+fZRUx0eRNd68TL+RuyN4AwOejPmdb/jZ+O/IbS9KXEOsTy6xus5gUM4kIz+Pb20vzMzh02ST8y5y0BfLaByBUmgl++ze+mKGAKAXdC+Yy0Oze0LsYH/rMeBbhuPdxI+TshgX3ub+AWgPA/n8VU6jAGIjBI5BnPYJ4oq8JjIGovUNYnqNmJQHYXTBzQLTsdXOGEV2uOvZ5cLv9ff/E/bicDiI7dSWo1cWX6qI010JlsZX+l7Wmx9i6kcGSJPHOC0vQ54eQ2yoE/5QSNictPG1C31Js+mUutmoLRh/fxgufBBeN0GsUCl6MDeP6A2n8L72AJ2JCjlv2QFUNuyur2V1ZTYhWzdvtIhp1dRwROYLfE3/nzlV3cmPHG1k7fS3Vjmp8dD51yrnKyyn56isQBLynTEEdEYGrqAiVVo3R7CQnXE+nT7+lY5uuVJcVkjR6CI+stlP0wj207dqZYt9A0Hhi9PI7gcgfJawH3LYWChPAbgGHBezV4Kh2H9stUFMKVQVgLoCqPNR5+8BSCJLIGGBxp1vYH3c/V/QIP3FfMi2Ow2pF9R9b8v5Vy5BEFze//yXegUHHqXlhYzuaitsroP5627KVW9Dl+yMOyOWVa1/lzWsvIW/XXvIuySLE/9QSyZ0usg8dJGXnNvpfcTX9r5hxWvq4aIQeYEyAN1cF+/FhRgFj/L3o6mlgdm4xV4f4YTxqTyt1uNMo/MPXHaMbDawCt1lmyeVL+HDPh3x98Gs25Gzgyb5P0lNXd7Gs4M23qJg3D4CSL7+qPe/y8cShVRKQX4PR5H4IGXwD8fb1oCbLwrih957cTSuUENzMHRxFF1SXwPJniDs4m7iR14Pi3PwnuVARXS7KC/KI7d2vzvnKwgK8AoMvWpEH2LcyC72XhugudV0fLeYaDi0sodqrkgevuhxBocA0oT/FC7fw1aN3csubn55TYu90OEjYsIbNv36P0ceX3pMvP207hV10uWxfahNGqE7NxN1JhK/bx9NJOVy1N5V8m3uWcF9CJvuqqnkvLoL84d2O2uWbRoA+gBcGvMA7w97BbDdz2/LbWJq2tPb6yqduomLePPZ20HHPPTo+G6/g774CW9sJVDqq8DS7KA7W124QDWAtcKd0sKxZ3EKfQBNQKMEjEMa9BgZ/mH+XW/xlzhiCQoFSpUZ0OeucF0VXvXMXG7nJ5fgFG1D9Z8vMH79fgcamp/e0cAwa92z/pmufpuc9N6G1SHzxxgO4zpG/48yD+/n6nptZ/vkHGH39ueyx507LzmT/cFHN6AE8VUrmdo7ht/wySh1Ofs4vZUelhX5bDzHK34sVJZU82iqYq0L8G2/sOIyOGk3voN7MWjWLR9Y/gkFtYEj4EIwrd+ASoGhSXyZ0bYvfUD98db746nzxxEiwNpD2prozjsj/PUfGPc9g27II4/CG85+cNgx+MOYVmHcL7P0Relx3Zvu/iHE67LicDpSqujb6sLiOJO/YSlVJMZ7+AWdpdGeXLsPD2b0sk/y0CoJbub3Hqm3VVB1UUh2eydjeN9UpP3Lg5aQlHqBi6U4+/+opZt32+tkYdi2i6GLl158giiJXPPUykZ27nvY9fy86oQdo76Hn2Vj3E/+99pEcqKrm2eQcFhZV4KVSMD341BNG+eh8mDNuDlcuvJKXf53FHWmtaF/hJG3GQO696bM6ZV3l5Sh9fBpsRz/qcjR+z1O5cj1+T57ysJpPp6mwew4sfhi8w6H1uRX2faEiCArUWi2W8vLaczVVleQeSQBBaDjv/UVCz3HR7FuVTeL2glqh33XwEFqnnrAuoQ3WuemGZ3k16RrEVQf4I+xzLp94e4PlTjdHtmxg9+K/KcvNZvRt9xDVpdsZ6feiM900RGdPA793i2VJz7Zs69eBMF3LJPZSK9XcuUrF+1+4aLsymYyBMQy+55U6Zap37iSxX3+SRozAmphYrw1BEDB2bkVNrhOpxlzv+mlHoYQrvgGjCVY+LyfUOkMolApUGi0up4O8pCMs/eQ9vrhzJknbNzNw2jV4BVy8W0Zq9Cqiu/iTvLMA8Wgm2qLCcgAioxregEWhUDDrsQ+oCVCR+t3fPP/V8eNYThe7F//FwvfeoMZcxYgbb6fLyLFnrO+LckbfEEpBwMcusSY5H51aSZCXjmBvHYGeWpSCcFI53CWXi4hNqZh7xxH72tt0Cq8frPXPTN6Zm0flggXoHnqoXhlHnjuyL+uSPniOHoXS34TH5bej8A1Ectip/PoVxLISfB77oK4njiiCOR90PqA5BddIj0AY8ggsuBf2/wJdrzr5ts4H8g9A2npQ66HVUPBvXpBdS5Bz+BDVFeUc2byBw5vWodbp6TBkBN3HXyJvNgLE9gwiZXcRBelVhLT2Jj07By0RxEXVD2T8Bz9vE/e8/g2vPTId3zUZmK+twkN3ZnI3pe3ZyZo5X9KmzwAm3vfoGQ9yk4X+KCsOFXDrd/WjcQXh2CT2/au6MbZjMDp1w4mIbE4XkkTtdUdeHliqie47Ct8GRB5AHXHMJl/x198o/f3xu+EGnIVFlH43B4XRSPA731D+zTuULd6E5dsVAATlZuPzwOtkTh5CTZ57cU603Y7fs58jiA7Y9zNseh9K3Wmb8Y2GSe+dvOml+7Ww53v483bQekJcw7n5z1ckSULc+zeK3V8gZG2se9GvNXgEwWWfuj/HM8CBVcuOjkuk/xVX0+uSqWh0J5+B9UJDa3RLl9PhQpIkahLViL5l+PqcOBjS19Of6CEDqflrFweTttOv88jTOk5JkijNyWbLvJ/RGoxMuPeRsxLJLAv9UVz/8nRZct9g8iut5Fe4v95flQTAfT/vxVuvpku4Nz4GDT56NT4GNSUWOweyKziQU0FMgJHlDwxBpVSgDg5G2yaW4o8+onrrVoJffAFtTN0Zh0KrJeiZpyl46WWchYUUvv4GglJF+a+/YEtyb0ZS/OlnKAwGjINHYezchvx3PqPgx/UU/twfSRQInD4Y89adFP68EWfeRILaZUBlDoR0hXGvg6PGvZg6dwr0ugnGvwXKZv7qHdUQ2h2yt8PPM+D5ilP6vM8lLFu3kjnzxtrjuCeHI0z51H3PKavh4B+QuRne7wr3HwSf0++il7BxLQBXv/Q2oW3jTnt/5xv+oR4ICoG0vcUUSrn4VAVj8SppUt0B3cew6q9dJMSfHqGXJIny/FwOb1pPwqZ1lOVmo1AqGT7z9noBcGeKi1roSy12Xl+SwNKD+VRa3bPiB0e3pX2IV23GR4AHRrdFFCW2pJbwx65sUostZJfVUF5tp6LGgYdWRVywu3xhlQ27S0SlVCCoVET98APl8+ZR/NHHpE6YiMfQofjdOBNjv2P+0b5XXUXl4iXU7HInRyp4xW3H97/jdvRdulCzezcVixZTtWIVVStW1dbz6ByM16iReN36DH4OO/k3j6N0XTpGqQSP/gNh5iL3KwlAvzth9cuw5SP37HTY403/oEpS3OJedBjCe8OYl0/q8z7XcJaVUfLZ55TOqZtqmoiB4HU0oM6/NfS51b0+sfFd+O5SmPat+yF6mrCaj63FXGgiL4kShzblkp9SQWmehcoSK1qDCp1RTUisD/2mxKBUNr50qPdQY/TWkJ9awcAREUAhKJu2ftSpbR+WKSUK01JO8W6OUVVaTOquHRzasIbizDTsNe4sseEdOtFzwmSiu/Y4qxu4X7RCn1Vaza3f7SS12MLoDkHo1Uqm9ghjQOuGXdYUCoGBsQEMjK17XRQlBAFeWHCI7emlvDe9GwbNsY9V6eWF/8yZeE+aRNnPv1D2009kzrwR32uuwXTvPSi9vRGUSkz33kvmDTcAoA4NJfiF5/EYPJiS2bOpWLCQoCefxDhwADV79lC1fAXlf/yB3eGH541PACCoNQR9/Ac14waRu92HGL8tqCxFbvs6uO3NY18BSxGsewMQoN8doPM+8QclijBnsjuq9vq/IebUUuNaHBZyzDnYXe484N4ab6qd1RwoPsD4VuMxqpset3BK49i2vfbz9p0xg6rVq3Hm5xN7aT7C4AaC00Y9D7Gj4feb4MsRMOAeGPQg6Fp+S0ZFE4TufGXtD4c5tCkPo7cGn2ADMV0DsNtcmEut7F2RibdJT6chYY22s3t5BuYyGz3HRxMWGIxNtRPB2DQfeZVSjcMgIFZWNV74BDjtdnIOH2L/yiUkbd+CJIn4hYbTYchI/MMiiOnZB68A0yn10VJclEK/NbWEq75wbwry3U19GNL25H8ZCoXA6sMFzN6czk0DWzGqQ8MRi6qAAEx334X/LTdT8MorlP34IxULFxJwxx34Xn0Vxr598Bw/DvOq1YS99y6amNbkv/oqZd/NBaBm9y68xo7BY/BgPAYPRhvbmoLXXif/5ZfxmTYNbUwMos1FwIOPkvPU6+Tv9CZc34Cb6IS3wWaGta/Clo/dM/1+d4Lep+EbFASwm6HjlGaJfI2zhvjiePYV7WNt1lr2Fu1ttM4LW15gSPgQBoQOYGDoQKK8ok6Lf7EkSRR//DEA4Z98jOeIEeh79CD34YexlmhQl2WAqW39itEDYdYWWPqEe3a/ey4MfwJ6zGy+KewEaPQG7vv6D6xVZ8HL6jSSm1TGoU15dB8dSf+prev8biVJ4vfXd7JzURq+wQZC2/g0+LuvLK5h0+/JpO4tonWPQDoOdrtTSm3LMRwK5Z0v5zBzxmT8jCfOGSPqVAhFFmz2GrSa5q195CUfIW3PLvYsXYDVXIVKo6X35Kl0GDICv7DG06WcDS6KNMX/Zehba7DYnMy9uW8dE83JctUXW8irsLL8gSFoVU3bMcZ6+DCFb72NZdMmBI0GfY8e6Nq1q29GADQxMUR++y3qoGMudS6zhZx778WyZUuDLo81pkDS3/ue9JJqrusfjcnzPxsb5O2DdW/C4YWg9XbP7vvdCfoG/kG+nQAuB9yyosF7sbvsfLH/C/5O+Run6ESUREqsJ7aXvj30bbRKLRW2CpQKJVtyt5BRmUGZtYzMqkwAor2imdBqAmOjxxLjc3xviuZSPn8+eY8/QdCTT+J3vTsIzFlWRsrwQWiNVqLeeQyh720nbiRnl3tm/w8zfoU2Y46Zyk4B0eok9/ktKDzUhD7dr/EK5wGleRYWfbwPp0Pkupf6o9LU/z8pzKhkyWcHMJfZ8PTX0bpHIJHt/dDoVVQUVZO+v5iUPUUoVAp6jY+i+5ioWm+4qhozX767GG1mAFVehdzzwmSM+mOeZk6Hg/j4LSSnHSArK5HifQn4mjWEXz2W6VPuadI92Guq+fud18jYvweAVt160m3sJMI7dDonFsovyq0E/8vW1BL+t/wIh/OrqLI6iTEZWf3QsFNut7DSSt/XVnHviDY8MLqBWWAjWLZvx7xqNZatW7EdOVLvuqF3b8I//gilV8MPJGdJCeb1G6gqLOaw4Mkhp55tZhXby8Epun+3/kYNb1zepeG3jbz9sP5NSFgAWi/oezv0m+WOihVFQIKVz8HWT+G2dQ3mzfkh4Qde3378aMMpsVO4pfMtmPQmfjz8I+/vfp8vRn9B/9D+DZbPqspic85mlmUsY2f+TiQkBoYO5Km+TxHhdWoLoY7CQlInXYI2Npao7+fWcUctfPIuSuatpvVjg9Dc+GXjjc25xO2G+Q+XfAA9bzil8QG4Km3kvbodgPDXB59ye2cTl0Nk3+osdi5OR6VRMOHOLgTHHN9caLc6SdldSMruIrISShFdx/RJo1fRvn8I3UZH4uHb8I5Mfy1dQ/Z8CaF3MbNuvhKAZSt+YMePP6Kvdj8URCTsnko8O8dww03P4u3pfvO1WsxkJ8RTXVGOy2HHabfjsFkpSE0mPyUJm8WMy+mk/xUz6DR89DljlvmHiz4fPcCy+Hx2pJfVHqcWWVqk3aRCM5IEUf4n56du7NMHYx93nmpnSQnV27bhLC0DAZSenniOHYtC2/AfdVGVjW92FLEu058jBRpcR4U9JsDAzYOCGNspGKNGxf2/7OWW73ZyXb8oXpjcsW5MQEgXmP49FMS7Z/jr33aLeqfLIWOz24Y/7Vu3u+b8O+HW1aCs6zkwJHwIOeYcDpUcIqsqi84Bnekc0JkQYwhDI4bWsbsPDR/K+7vfp8J2fK+dCM8IpsdNZ3rcdIqqi1iQuoAv9n/BZX9fxh1d7+CGDjegVjbfe8GWkkLOgw8h2WyEvPJyveyfgr0UgMyP1mKqehSvW55GMJzgjU/xnzEsuBe6XOleDzkFFB7HAvbsOWY0YR6n1N7ZQBQl9q7I5MC6bMylNqK7BDDkqrZ4+p04n4tGp6L9gFDaDwjFanFQkm3GbnPh5a/DJ9jQ6ELtpeOG89qmH1Ae0lJRWcLsz57FuSsDp4+A/8TBtI3rSceYXngbfHA5neQlHebg/kVk7N9LfkpSgxHHPsEhtOreC72nF7G9+hEW16GBns9tLpoZvShK/Lwjiyf/PADA9F4RvHHFqSf4d7pExr63HglYdv8Q1GdgIa2wysrn61L5YVsGdqdI/9b+9Ij0pXukD13DffD3qPtgsDldvLoogTlbMvj+5r4ManOCHCmFCbDxPfcM33H0YTjgXre3za/XuX8e/eJJmygsDgsjfxtJnF8cs8fNbnK9AksBb+x4gxUZK4j1ieWZfs/QI6hHo/Ukh4OaffuoXLyY8j/moTAYCH3zDTwG158pi1YrFR88RemfS7GXiagMEr6je+B770sow/4TB+G0wxvR7s/II8id5hlg5mK3Lf8UqDlSSsm38QCEvTwQQXV+Lc5KosSGXxI5sC6HsHa+9BgTSWTHk88d1Vw+/eo3HJsLqXIsQmMXsHbx55773sNZVkVRZjqlOdkUpiWTdeggDmsNgqAguE1bojp3J6pzV7xMQag0GvdXC2/gfTqRTTdHufKzLWxPL609Tnl1AsqTiHj9L0sP5nHH97v5dmZvhsed/tD0ce+t53B+FVN7hHH38FhiTI3P+GrsLnq8tIIreobz0pQmpC3OPwBfDHdnr5zxs9uHfuEDsPObUxb7pzY+xeLUxey8didKRfP+idZnr+eVra+Qa8llRMQIBoUPIs43jkBDIHaXnRpXDVanFavTSnFNMfYPv6LdkoTa+n4zZxL0+GMn7ENyubD8/jmlc77FkmpGUEp49w7H7+4n0Pb6l9919i74akTdypd+7A4uOwVyntuMZHPhe3kbjL3PnkveyWCvcbL863gyDpbQdVQEg65oc0b7T9qzi6UfHcRu/g2AsGH98JGMpO/fg6XM/b8vCAp8QkKJ7NiFqC7diOjYBZ3x/Htr+i+y6eYoX17fizVHCll0II8Vhwro+NxSOod509rkQYzJSEyA+3uEn6HJM3OHSyS50O0dkVVWfTqHD7gF+3B+FbcPjeGJ8e2bXE+vUTKsnYll8fn1zTcNUZENogMu/8ot8gAT/geCEjZ/4F60Hfxgs8d/uPQwS9OWMjpqdLNFHtxmol6X9uKL/V+wIHUBq7NWn7B8Z2+RZ/51XDp7Nqb770OhO74JQVAq8Zg+C4/ps7BuX0npR29QsT2L8mvvxhjrgeneh9CPucodIfxfCg41+57+i2Rzuwkaup9f+WyKs6tYOTuBslwLQ65qS6ehjbtJNoTDbiUz8SAleblYKqxYq5w4HU5cTif2GgU2iwp7tRqb2QuF0o7O04Lex4q52ANreQAqoxGnoESscpGzdislHp5Ede5GVNfuhLRui09I2FkLXDpbXFRC721QM6V7GJO7hvLbriz2ZJaTWmRhZUIBxTvsteVUCoFIfwOt/I146dXo1EoMGvfXv38G+HJDGsmFZoa3MzGu0+mffaWXuM0pncMa8X9vgHGdgllyMJ89WeX0jGpky7J//O9t//I1Vihgwlvuh8Cm96D3Lc3yI692VPPg2gfx0fnweN9mBGz9B4PawP0976d3cG/uWHnHccsNCB3AS9Ne4udLfmLj4i+ZYOxD3y+2UDH/L3yvmt6kvnR9RhH63SgCs5LJv/8GquJLcb70MjFjroLvL3cXUunc9nqDnzsi+RQx9AzCVWE7r0w28RtyWP9zIlqDivF3dCa6S/NSKFebK9mycAnZCU4sxf5ILg3g4f4SXCiUTgSFiEprRW2wYvSz4bTaUekcVJf5Yq2yofPNILDbCnxiNqDV+eOjuIng8OEEREahOIlJxYXERSX0/6BQCEzvHcn03pG15yqqHaQUm0ktspBa5P6eUVpNYmEVNXYXNXYX1Q5XPU/GKH8DX13f67j+8y1Nbrk74s5H3/wMm8PjAlErBebvyWlc6D2PRoamroW4f+XBFwT3TP7rJbDrWxh4X5P735izkayqLD4b9Rl+ulNPBX2w+CAAK69YydqstWiU7s8ksSyRw6WH2Zq3lZuW3cTlbS4nKVpNTon7YZ7//PNULNpM0JP3om/ftIRlNX+8gzmhBLXRRegIHXw2GCoyIW4SpG8AawXYq9wuqidJYmIiP/74I5MmTaJXrwbfwM9JCjMqWf9TImFxvoy+qQN6j+b9bR7ZtY91P6bhsJgwmgqI7FZCSGwAwZGt8PY3YfDyRvGvhXOXy0pZ2RaKildSXLwau70QQVDi7d0LU8AoAgKeQq8/PTEY5ysXpdA3hLdBTY9IX3pEHl8AJUnC5hRrRd/qcBHha0BzBmdePaN88TGo+WhNEgNj/Zv1x+ylU3NFz3C+35bBqA5BDD1RoJhXKPS4AbZ/Dplb3EFT1SUQ3se9pyy4NxZvBsnl7tw9PYN6NlKyacT6ujfG3lGwg+lx9Wfo2/K28eTGJ3ln1zuEeYQx2XgzmnYhiFUZ1OxYTtYdKbRdt7DRfsyzXyH7i9XofB1EDilBKRRC/tGLoss9o+eoF1HPG4/XzAl5/vnna39euHDheSP0xdlmln5xEL2XhjE3d0RnbLpJpKq8kNU/rCX7QABaLyujbg+gXferGyzrctkoLllNQcECSkrWI4o1KJVG/P2GEGAaRYD/MNRqnxa6qwsPWeibgSAI6NRu883p2au9cXwMGh4a045n5h9k0YE8Jh1no4Xj8eykjuzJLOf+n/ew8N7BhPmcwBVw0nvuxdicnRDQFrQecOgvQHC7ZLa/pFl9t/NrB8DyjOVMbj25WXUbYnjEcNr7teeD3R8wKnIUOlVdu3vfkL4snbqUXEsu4R7hlH5zCOfAKwl+qBeZNz9M9dYliA4nihPsCWzdtIjsN+ei83YQObQEpeY/r3QKJUz+CH6c5k4HfRJun+K/EuqNGjWKHj0a9yY6FUSXSEVRDen7S8g+UopPkIHW3QMJbu3d5HTcdquTg+ty2LEwDY1BxYQ7ujRZ5J0OB2t//4OkzR6ITj8ieiQy8pqpGI111yQkSaS8fDv5+X9RWLQEp7MKjcZESMhUTAEj8fXth0LRsOuxTF0uKq+bM40kivV8tVsClygx6cONVFTbWfnQ0Dq5dZpCapGZSz/aRKiPnt/u7I+XrhniZLe4Z7EnkeNFkiR6/9Cbq+Ou5qFe9fPunww78ndw07KbeKLPE8xoP+OEZQs+2gMuiaD7enCk9xDEqiICHngR/5mXotDWNzdIdhvpo3riqLATM74Ila6BXZ363gF9boOfrobiI9B2PEx6x/1G1Azy8/Px8vLCYDiFfQOaQOL2fFZ8c2zBWKVWIEngcorovTREdfDDN8SIT5ABtU6J5JIQRQnRJeFyilSVWilMqyTjYAlOh0hUJ39GXN8eg1fTzDXZyYdZ8e0Bqkv88YvOZeDU7kS27VinjNmcSH7BX+Tn/4XNlodSacBkGktw8BT8fPsjCBe3vf14yF43Z4GEjWtZ/OHbTH3iBVp1axlTxT8oFQIvTO7IlZ9v4dO1KTw0pl2z6seYPPjsup7c8M12Zn2/m29v7N10/3/NyScd25y7GZvLRoD+1PY6lSSJe1bfw7rsddzR1b0YW+1s3ONJ19aXqtVZiHYXPlfNpOy7Tyl+91lKv/wQ0wOP4jtjYh1TWOVnz2ItdBHav8It8gFtod142PY5OK3uQts+c3/9Q+ISeGcJDH0MBt7f5A1fgoNP30K+JEkc2ZqP0+5i3U91dzHTeajpNSEatVZJ6t4iMg6Vcnhr/nFacmPw1hA3IIR2/YJrt/JrrP+U+M3sWX6YwuQIlGo1fadX0Wv4MTdUm62A/IIF5Of/hdl8CEFQ4uc3mNjWj2IyjUKpPL0PwAsdeUZ/mkjZtZ35b74IwK0ff3Natn677+c9LDmQzxtXdOay7uHNrv/7rmwe/m0f946I5cFmPiyay/Z9+7l9z0wivSL5buIcvLXN9xr6B0mSuGbxNRwoPlB7TqVQ8UCPBxgbPZYgY8ML4+ateZTPTybkyT4ovbSINjul3y+g5MtPEMtzUQa2wu+GG/G7fgqCIJA6uAuCQkmr9XsR/h00k74RZk+EbtfC3u+PP9DgLjDjl2bP7luazfOS2bPcnT9IbzqCXhvLlU+MpDTHwqo5hyjJseAf5kG/S2OI6uyPvcZJeUENLqcLhVKBoHDvsKZQChh9tM2yw+dnpLFiziYqc0NRqGxEdDEz+PLBePsH43CUU1i4lIKCBZSVbwMkvLy6Ehx0KUFBE9FoLs7Nz08WOWDqLOCwWfng+isAiOjYhcuffLHFd5Yptdi54/tdbE8r5cpe4bwwuRP6BpJFnYgrP9uCQxT5c9apRXOeiLJ8C9++vpofOr+Er2jirRFv0L11E4K2TkCVvYoBPw0A3CkTDCoDR8qOoFFo+Hrs13QL7FavTsWSNKrWZRP6fH8UumO/C6e5hqJ351K54CfEynwUnoFogvRYkzMIvedyvO9qJP9+TTm88a/t/Sa9C15h8PvN4B3mThtxCm9Cp8oX96/DYT2Wwvf2j4aiOpp8TxIlknYVsO3vNCqLaojpbmLk9e3R6E/+b1UUXeRnHCJ+02GSNnujUNmIG2Knz7hxaHRQXLzKvahaugFJcmAwtCIo8BKCgi7BaGy55HUXG7LQnyUWvPs6iVvd29J1GTWO0bfe3eJ9OF0i761M4uO1ybQJ9ODjGT1oE9T0fTBfXHCIbzalkfzKeFQnkb6hoqiG1d8lUJRVhd5Djc5Dg8FLg1+IgbC2vngF6NnyZwqpe4vwnFrK+9lv4BTs+NaEcHmrK7hn/C3N7hMg8chWpm+7g5mdbuS+Hm4Xz/SKdK5ZfA29g3vz3vD36tXJfXELmkgvAmZ2rHcNQLQ7Kfl2PmU/zMZVmIJCDW137UXQNGHB7/Ai9+Ysnae5g8wAUta4d/XqeweMf+Ok7vNUcTpcfH7PujrnBkyNpfuYyDrnXC6Rfauy2Do/Ff8wI1Me7IG2GWLvdDqI37qexB05lGZ64qxxv7H5x6Qw8vrBoMymoGABRcWrEMUatNpggoImERR0CZ4eHWVXyBZAttGfJTqPHOsWekEgYeM6Rtx4R4vP6lVKBQ+PbUefVn488MteRr+7nomdQ3hoTNsmpUaIz3W7Bf68I4tr+zVv02lJkljy+QEqi2po1zcYu9VJjdlBZXENmfEl7F6WWaf8tSOnMiSvH0/NfYNMnwS+LPiQuC3tGd2/eW8TGWn7uXrDrTjVsHjTbMzbt/PIDV8Q7R3NFW2vYHb8bJalL2Ns9NjaOtaUcsRqJ7p2x/eXUmhUmG6/Aq+2AuKftyPGXd80kQf3Hro3r4DgzsfOtR7uXqjd9hm0n3zKOXBOhCRK2K1OrBYHOYnllBdUYy61UpxTP3lfdZW93jmlUkGPMVH4hRhZ8ukBlny6n8n3d2+SF07y/l2s+zEFa3kASq0PfpEVhLZRYWrlQFSlcijxE5zOStRqX0JCLiMo8BJ8fHohCOdPQNj5jjyjP41IksTcR++hKDMdgOkvvEF4XMOzyZagoNLKsLfWUuNwv6bve3YM3obj21N3Z5Yx9ZPNAGx8bDjhvs1b8MpNLufPt3czdEa7ersCOe0uso+UYat2IrpE/EI9CIo+5qlTVl3OhJ8uQSfq+euaP/AyNO0tJL84nZk/TyXH08GEimgKXeXs9CunfZmByaFjmDjmLu7f8hj7ivbx7bhv6R7YHWtyGcVfHUTpryPo7u4ojjNTlVwi5v89gYflC5zKaJQPrEHh6dOsz6QedgvSJ/0R7Ta4aydKY9PftppCeWE1m35LIjOhFNF57H9ZoRLw8NXh5a8jtmcgHQaFsuzLeNL2FXHbB0NPmAUyfkMOa384QsfBoQy4IhaNtuHPq6q8iDW/LCFrTyhqYyXdJ+jo0LczhcV/k5f7OzXWTJRKIybTaIKCLsHPdyCK/2b8lGkx5Bn9WUIQBLqPn8zyzz8AYP+KJadV6IO8dBx6cSyXfLSR9OJqDNoT2+uXHXR7Vyx/YAjhvgbsVidKpQKlumkzrcRt+SgUAm161V9oVmmURHeuu5hWWm5l4cYMPAxqpgyL5vEOT/N04sM8Pf9VPpjxWqP92arNPPb9dRR62Pk05EEG3XATAL/Pe4V3qn/mjZr5JHyzh3funMOwX4cxP3k+3UzdqDnk3gTFe2z08UXebsX65gw8nauoFgeie+jHUxd5AI0Rq8WF3pHPlrc/ovvDDzdrMfNEVBbXMO+tXYguic5Dw/Hw1aI1qPELMRIY7VnPHFJdacPbpG801W9c/2B2Lk4nfkMuJdlmLn+srnbYa+xs/Gs5RzYJiI5Qwjrn0n2CnrLKP9i6/T5AxNenHzEx92MyjUGpPPubclzsNFnoBbfz6k4gR5KkSYIgTAOeB9oDfSRJanAKLgjCOOB9QAl8JUnS8XeouABp138Qq2d/jtNm48iWDQy55kY8/E5fyta0YguJ+Wam9Qpv1GWypNpON5uSH17cxjPvD2fxpwfIOVLGre8NQaM78Z9G6t4iDm3Ko9OQMLQneGv4N+PeWEuh5H7beHC5O6OkISqSNYaFHMi/hc7BJ05H8PkPD7Dbt5xHVJMYNOam2vNXTH2Ky6wP8fiXV7DAN53L47di0puYlzSPJWlLUKLgTq9pjNvshaFL/WhgSRSxf3QdeucqzIabMT70NkILpJt21ZgpmP00oY5sShwR7CvtQeqbu5jyYHeM3qce6JOwOY+aKgdXPdsH/9ATm+kkSUKpUjS4s1OdMbtEtv2VhrnMBkB+WiXbF6bSdWQgaYf2kLo/i6y9HjhtHniE7SGqz37Q7SU5zYxWG0x01B2EhFyBwdA8M6DM6aU5M/r7gATgn/fvg8BU4PPjVTj6cPgYGA1kAzsEQfhbkqRTT/F3nqDRG2jdsy9HNq9HdLmIX7eKvpdd2Wg9h91GeV4uLqeT4NZNS/VaWVzE9P+txo4Ho1rVN8NsyS3jj4R88ittlJrtHEoo4pEa92xr9mObsFmcAHx5/3r6TYmh57joem2U5JjZtTSDpJ0FBEV70XdyqyaNTZIkikUXESoVl7QL4rvDeZhFkZq8aRijP+G6xbfy3YSv6BJ8fK+L5dW76FjjwfX31p8rKHU6Hpv+MZvnTeKR7U/zv4mfkFiTybfx35JjzuGtsNkMTehFxbJ0vMbUzYNiX/0H2srlWCPvweOmuh42lYnbqTqwDBQqBEFBbkoKPiFh+ITHoDTFog9vj9bo5W7vX22WbFuNesndhJJDjsclBN75BcMPVrLy20PsXpbB4CubvxvZf6l9M2jE+mqrdrBydgLZh8uI61ffX18SJcoLq8lLqWDvyizK8ix0GBRKdBdfFn8Sz46F6exYnAyiCgQTxqBDhHVcgN4/DUHlQ6BpAoFBE+VgpnOYJgm9IAjhwETgFeBBAEmSEo5eO1HVPkCyJEmpR8v+DFwKXDRCDxDcug1HNru3nBPFE+9UX11Zwfw3XiQvJbF2L9ioLt0Zdv0t+IdHHvfztpSX8furzxJWE06Rdzdu/PkwuoUp+AcaiQvxpLDKxv6DhQj/Cu7U+Wo5qFLSqdCFzeJEa1Bhq3aL/db5qRzamIveU4NKo0QSJapKrVSVWFFpFHQfFUmvidGNzvz/YfGGDEQBJrYJ5NHru/Eo3Wqv/bg3hld3P8z1i2fy92V/EOlbf9ZdU1lKho+DG2xdj9tHQGAUH7R7ilmJr/DIwll81OdNcszHsklWK2pQrslC39EfTfi/bOXbv8Il+KO99pk67RVu+g2/ZbfhpTj2oYUCVAFH445ckoAIOCQVBZ69MQyehWPdHEKqV2IhgPzBcwkb5U730La3nqQdBRxcm4N/mAcdBp68f72txsnhrXl4+GrxDT7x2sr+Ndmk7y+mzyWt6DAolNS9RWQfLqO60obV7KA421z7e9d7aRh/e2diupuI33Z0m0SFE782K9GbkjAEJKM1KDEFjCEo6Bl8fQfIdvfzgKbO6N8DHgWau5IUBmT96zgb6NtQQUEQbgNuA4iMjGyoyHmL3vPYImRRRlrtzw6rlerKcpQqNVWlxVQWFRG/dgV5yUfoOHQUkZ26kJ+SxJ5lC5nz8F0EREYz7emXMXj71Gk/8+B+ln/xAZbyMj58fBafrU/lh3QVVrODHHslOanlAPgEG3nj0o609TOyv7CKCa0C6LV0H66D1XROt9NzfBTdR0dRVWoleVchWYdKyEpwb7/o4asltI0Pcf1D6DQkrMkh7//wyeoUDJLAXdPqr1HM6DYEl/Qmb+6/h1lLXuLvq96rk60QQK33wGRWsFTazbVZhwmOiGuwn57Dr+IDycV9yW8wY98DoD72YMzQ5tFZEYfkcUyYnKmH0di2YY++He2/PGwkUcS24VNUCpGSST+g1BoQHTbK83JQqZSoFU6ksnSE0nQcdisqcx7h5i0ol7oXt3OCrsc04wWCfY5l6RQUAqNv7sjSzw+wZu5h8lIq6D+ldbM/S5dL5KsH3CI88IpYFI2YmTx83TmAti9IY/sC99+fWqustem37hFIUCsvgqK98A0x4rS7SNpRQPoub6AEjbGYkB7LMJlGERR4D35+A+UcM+cZjQq9IAiTgEJJknYJgjCsme03NP1s8EVTkqQvgC/A7XXTzH7OaSoK3YueHYeOJH7dKr6460Y8/U2UZmditZjrFhYEIjp2YfRtd6FUqQkN9SaiZjuhuX/jFLez8qEdhI65Fb82XclPTiJl1zYK01LwDgpm2tMvE9q2PS+268DcpdsZaC3lh8vGklllxVOjxl9/TOBivA3cveQQhUYFK7sY6JJpZ/O8FCLa+xEQ7kn30ZF0Hx2J0+5CqXJHR54s2/YVEG+1Mj3KhKexYVG7rvsw/kqczBHr31z/50t8f/lzda6r1Bre6PkCdx58htv/mMGcK+fhExrdYFt9R1zD/I79eO6X29jsXVh7/iXfNRTnBiK+vooOKjU9/D2Y6lxCZ0FC0a9u9suMd6cQbd1FrqYDob0m1Z4/UXLl6rwk9r75IjbBxPDn32mwjFav4pJ7u7F9QSq7lmaQsruQS+/vXscj6URUFtewd9WxuVPn4Y1HRMf1D8boo6EwvQqVRoEp0pPgGG+U/8q6KooSmQdL2LEojYwD7jw2Rm8N7Qf5E9ZRSWyX7SiVsrifrzTqXikIwmvAdYAT0OG20c+TJOnao9fXAg83tBgrCEJ/4HlJksYePX4CQJKkE7pYXCjuleC2Tc95+C5UGi2jb72L75+4H4CIDp3ReXgS3qETCqUKDz9/vAJMqJyV1Oz8FVdhEsaiHfgry+q16ZIE0s2+rC9shTayO+0GDKbL6PGo/zUjHTF/BXlKDasGdyXUx6deGy+tSeJjLAwzK/hhQifS9xWz9IuDaA0qZr45CFULpl6++c0NrCmpZNPDwwgxHT9CVBRFJv10H1nOtUwOfZhXRt9Qr8zKNd/wcNq7dK7wZM59G+vN/P/b3pxfnuAd++Lac10KH6W1sQ27i8wk1NjoLCQzX/ss+/2m0f7mt1Ab3X72Zc+1QiVa8Xg+C0HZtBff3Ut3smV+JUplGXd8fHmj5QszKln86QFqzHY6Dg4jrl8wpsj63jKSJFGUWcXeFZkk7y4CSUKSYPJ93Yhof/J5/R12F5kHS0jZXUjGwRLsVhd6T/cMv02vQEJa+5zSA17mzNJikbFHZ/QPS5I06V/n1nJ8oVfhtmaOBHKAHcAMSZLiT9TPhST0GQf28vvLTzNu1gN0GDKC0pws/MPrm6YqkndT+fczBJdvRq0QESUoEUKwRQzFc+D1eMcNxFVZQFXiVgqXfkCkbS8CUDnsdUwjb0V0Oag8uJbq5C048w6xDz9mtZuJh7WGDyJ8GN3tmG07tcjMwP1JdLEILJzYpdY7Z+XsQxzZmk94nA+X3t9yqXKveHENh6trOPj6hEbLWh0Ohv1wJWYxh69H/UzfyPqLs7N/eIT/OZdyo7UnD94+u9E2n3n/Uub7pAKw6vINBHr4AGCucbB6bRpe2+5hGFupRkdy8EQC/IIIPPgZhbo4Qp/c0uT7/PaRX6mu9GP6M10ICG9abiNLhY1tf6dyZEs+oijh4aclINwTL38dCqWAudxGQWolVaVW1DolHQeH0XVEeK05pjlIokRmQikZB0sozqyiKLMKp0NE56EmpmsAkR39ie4a0Kj7pcy5yWkRekEQLgM+BExAObBXkqSxgiCE4najnHC0zgTcNn4l8I0kSa801s+FIvSSKPLTM49QWVzILR9+jUpzzGyRUpDIgdTtJKeuIyhvGzOq3FGkebouGMY8hmfbfqg9j++GWXJwI8IvM/BTVpCnaIWXIxej0lanTIWoZ21AHzb7dsNWY8TgF02QwolJ4+RhQyce1fhy/6BjQiqKIt8/vZWqUiuDpsXSdWTz1krMFgf3frqNzUUVaBQKwnUaqmxOskQnnfQ6Fj43svFGgL15yVy3dDpGqR2bZ/5QLzpTFEUe/Hgiq7yyeUI5iRnXntgHv6akiD4L3Zt4H7jhQL3rLlFi5+ZV2LZ8Tl/zGrSCgzynD953LMUQ3vR9eX96YR6leT7c8r/+aI3N8x23mh2k7isiM76E8oJqKkusSC4Jg48W/1AjkR39adM7qFlpCQBcDpHCjEpyEstJ2JJHZVENKq2SgDB3AFtUF3/C2vg0aueXOfeRc92cJXYvWcCa2Z8zbtYDtO7bnxU7/mRF0hL22I9QrrfWKftTqQ+tpn6MMapLk9u3VZSQ++FlBNoOU6lvjStmJPq4YXi17UPR6m+Q9v2CtzUFj/88AAA2eXdjWdz/eHF83b8Lc7mVuU9tQRIlpj/TuH82QLXNyfdLkvhuRybZLie9jAZwSuTZHQhKgWGRvtw9tSPBAU2PvL1v6f9YXTCbS4If49Wx19a7brfVcPPnYzjgUcZXHV+hV99LT9jeoEc/IFzp4ufXHjhhuYysLL759C1SiWHuq/c3ebwAn86aj8ulZ9Yno1Aoz56bod3qJHlXIYnb8slPq8TlcHsNBcd402V4ODHdTXXs8zIXBrLQnwVWJyzhqbVPEFqsxzcgmL2qNGwaFwoRWjmD6Ovdgy5hPfDU2bnr4FvcFjiAe8YfNyTh5JEkLJkHKNu5AFtpBpUOkQDLASIsh1nd5ytGTJhWr0ryrkKWfXkQrVHFjW8MOq4o2GqcfPTrQeYm5FGOiA8KbukZwd3TTi0zJYDNaWPo99MxS1k80u11bug+ul6Z8tI8Jvw6hiirB3NuX4lGf3z7//QHvqYEDSvfva7Rvoc/8T1pki+DvS28c/t4TH5Nczb7dNYfCIKCOz6+rEnlWxJRlCjKqOLwljyObM/HYXXhG2wgsoM/oW18CIn1Ru/Z/H2GZc4fZKE/w+SYc7hqwXTK7e6EYUpRoAutGRM9hkl9puNjPLaAVmktZ+AvgwH4pvO99O5x62kfX/6bV+Bp3o3+uaTjzjxXfnuII9vyCW/vy6X3da93ffPefB77ZR9ZkpMIlZo7+kUzfWxrVOqWm8keLsriqr9vwiVU8eclfxBrCqtX5vufn+IN2990Lvfkg6t+JCAousG2HnvyK+Y7/El4Y0qjibqS07J5du46tlo88aOajS9ORdfADlT/Ze6Tv1JR4sPwa8PoOPj0pbr4B5dTJPNQKYe35JGdUIrd6vaQiu0ZSMfBoQS39pazQl5EyEJ/BrE6rVy/5Hqyq7L5adJPmKvKCfePxlt3/I02nl80kz+KdwGwYsKvBJuabhc+GUpf6INT6Ung06uOW0YUReY+vRVzqZXB09vQZXhE7bX0nErGfbABJQL3DYrhlontmrzXaHNZkriTRzbfglJUMcpvKLf2vpW4sLo+9L/88jxvmP/At0bB12O/IbpN/R29Pnv7B14v9mHjvf0ID21aCor3vl/GewedvDfKxJRRfRotX5Cey++vbwdJy9hbWxPb69SjX/+LucxGVkIpWQmlZB4qwWZxovfS0KprAOFtfYlo74fOQw5guhiRk5qdISRJ4oUtL3C49DAfjfyIKK8o8Go858fzE2cTtegW3ineBqLjtI9R78qjzKvDCcspFAouf6QHc5/ewsZfk4ho74dvsNs08vqvB7AK8Pu1vejVqeV3zvo3Y2N78MhPN6PwX8tyxXKWr1yOh90Db7U3AwMGcufgO5k+/XlMy8K4L/8Dlm6ewx0NCH3rEB8ohsQjWU0SeofTyV/xxYAPPp5NW1sIig4lIMxFcY6W5V8fahGht1ud5CaW14p7Wb57y0S9p5qoTv606RlERAc/2eYuc0JkoW9Bfjz8IwtTFzKr2yyGhA9pcj1JklhZtIdwUSI4sHPjFU4Bl82GXlFJqUfje5R6+OoYObMDK76O58//7eb61wbwzDe7WVpQTm+j4bSLvMPp4upvVlJljWGqrh03DA/k7YVvUygUYlfb+bXkV+b/MZ9BnoMYZnQnRAv3bzj3TpvWIXAgj5SMQkYcp7+Kigp27D7EuvhsFmeJlAg+BEuV9OjQtHw+ACW57u86w8k9sCVRojCjisxDJWQllFKQWokoSijVCkLb+NB+QCgRHXzxD/WQfdxlmows9C3EppxNvLXjLYZFDOP2Lrc3q+6SDS+yX7DzQuiwOomxTgaxIBP7thUo2vRD3a5DPTFQajQ4JC1Ulzapvba9g0jeWUDavmKue3QVWzQOehkN/PBY0x9kJ8uYFftJaBOEMspJscKKTRnI93cd26N1zZE1fLrzU1ZbVrO6ejUAv2YuYBL1PWvC2kajc6aTWlBT79r67Qe5/5e9lKr/2ZTEQLBQxbN9jNxw6fhm+ZVf/ugA5r21hWqzD6u+XcXIGxt3KXU5RXISy8g5UkbSjkKqSq0ggCnCk26jIwhv70dIa+8WXf+QubiQhb4FSCpL4uF1D9PapzWvD34dRTN3zkkrdWfI6tXx6pMeg1iYhePPV9Hk/oZOcMBucBKK03cAQtuRqPqOQ+nnh6BQUK5qj7F8e5PbHndbJz56fAM7FTX4CAp+efLEG1e0BKnl1SSowb/aSYSihlUqIyuSCok5kMLVJm9u7hLH8HbDGd5uOAeyD/Dh5g/ZUrOFPcYiNk+7AtPlVxA2dCiGkBAAVL6+BNgqKaiqb792SUKtyD/R34eRvdsTG3pyEadBrUK49uVR/PTCcg5v86OyZAGXPjixXgSv3eokM76U1L1FZBwoxm51oVAIhLb1oc/kVkR18kfvIXvJyLQM8mLsKVJcU8w1i67BLtr5aeJPBBsbN4n8l9y83Vyx9DqiFHrmzNiARt30YBtXYQ7OP19Fk/sr4MTmNQHFoBuRsvYgpK1FZdmJAiuSpMCh7ogYMoTqijT8KhdTc9s+9MfJF/Nf8nIqGfvBRixIbH5oGEGBp3ez6ytXxbNe4aCbU8lPQ+KoqLbwwf4jLKoRqdDqMThsjFS6uLdjLJ2D3Sak7LwUNr37Iu2X7EHtcJtOrEYjtvBwAq6/jlvXmwlXu5j79k31+vv6txW8tMu9xd6rg72ZMXHQKY3fbrXx43MLsFT44R1QylXPTkF0QfLuQnf2yIQyXE53VGqrLgG06mYivJ0v6kY2i5GROR6y181potpRza0rbiWxNJHZ42bTMeDkXepWrHqcB7MXcZ9fT265ZHaj5V2FuTj/fA117i8IOLB5jUc56SnU7eqOQXLYcO5Zh7h/GYr8DagciQiC+3dean+AYmmUuxwgCeDSKMGoRlArkCQJREAAhUHN7goLz5SW0tWl5Pc3RqM8jUFBb+3J4L2CYlxaJYJTZILWwKcD26JA4qf4RObkFBOv9UAC4mxmrg3x47pO7dCqVFirqshYuoyKvXtxJiWiT05BW13NlRNeZEjOPj75pW7Oeclux2WzEfuKOyNkX0MFvzw745TvQRRFfn1pHiV57rcDlVaJ0+bC019HTFcTMd0DCI7xlqNSZVoEWehPA9WOau5adRe7C3fzztB3GBnVtPD+4yJJ3D13ALtclayfsR21tuEZs6soH+e811Dn/oSAA7vXWBQTn0Id17RFXLE4H/uWRTi2raNSnECZLhokyb00IILS7kLjElFyVPxxpyBVHV07mIuNz7HxdMcwbrmu26ndc2NjFUW+is/l/fQCSjyUaKwuLvf24qVerfDQqDhcVMKHBxNZZhcwa3R42q08ZPJgTHQYMX7HNgG3VlTw0etf8JHUgaliBp1jw9mdWsRhq4ocpQdWlQbxX+a23oYKfjsFoXdYraTs2saRLRtI3rkbnc/dAHQcHEpc/xCCWnnJ/u0yLY4s9C1MjbOGu1fdzc6Cnbw26DUmxDSerKspfPLLZD6rSWXXtbtQq+qmhJVcLmxfPogufzYANs8xKCY+jTru+BtxtCQOix1HjQuUcNNHm9ltsfLHVT3p3K35pqrmIooir+7J4JuCUqr1SpQ2F2P1Rt7sE0OAXoPN6eSKlVvYqTEiHRVsD7uVKNFBnE5FV6OBV+dl12nT01FNnGQmyqjAS69GpVKSlhiPWSFx/dCujL/mimaN0eV0kL5vN4c3rSd551acNhsevn7E9hlAdLf+RHfphFIlm2VkTh+y0LcgJTUlPLzuYXYV7OKVQa9wSetLWqztuX/P5M2yXTxsGsC1Yz5CqXIvHDqS45F+vRONfR8ORQxM/QJ1p94t1m9zKSg0M+Hd9XgoFCx6fBgens3PpHgyiKLIRwdz+DirkAqDW/Djh3XBR+f+nCx2O2vTs9mQX0x8tY00SUmxzoBQ7US7oYAQwcqsaAU9O7cirm/nelHBFakpfPfw3WhdLmZ+/zsazxPniBdFF1nxBzi8aT1J2zdhs1jQeXrRtu8A4gYOJTyuI8IJ0ijLyLQkstC3ELsKdvHIukeosFXwwsAXmBQzqfFK/2Hp/qXszdnL7YNux9fTt841R3UJs34ZzVaFgxEKb54a+RFeq/5Gm/whkqDG0eVJNFNmnRPisXZDBjcuOsglfp588Ojpd7X8N98k5PJkfiF+FhcHJ3Q/bk56s9nClMXrOWgK4baCDF686sSJzwDiv/2KpUvn09rLnylfzql3XZIk8pKOcHjzOhK3bMRSXoZapye2dz/iBg4hqnN3lCrZmU3mzCNHxrYQ7+9+n6KaImK8Y3C4HJjtZjw0jWd3/Dfv7n6XXCGX+b/P58OhH9I75tjMXG3w5/MbdjB38W28XbKd1Suu48ecfNoa+6G69iO0oY1H2Z4phg2O4rZDBXyeVsSAeYe4auqJI20bIq/KytqsUnbkVHCkoIq8shpuHhjNXT1OfJ9fpxeCDl6OC6fM5sJfX1/oS0vLmLFsI4cCw3jVUsRNTRB5gI433sLGRX+SUllC0b69mLp2A6AoM53Dm9ZxZPN6KgoLUKrVtOrWi7iBQ4np0Qu19sy81cjInAzyjL4Z5Fvy+TP5TxamLCSzKhOloCTCM4Jo72hKa0oxO8yEGEMYETmCzgGdaeXdCq1SW7vwVmGpYMQvI1AKSpDAJbi4KeYm7hh0Rx0PlvzCHCYunoRdcPKd9zV0m/zoOTGL/y8Oh4tpL6/hiM3GXzf2pW27gAbLpZdXsz6rjN25bkHPLammqtyGaDu2UbqkAESIjfNn1cx+J+y366LdFBjqfh7+NSIhShWxRi0GhYsfbW5XyXftpVw99nixsA1TtG8vP770JBqFgi5TryRpx1ZKsjMRFAoiO3UlbuBQ2vTpj9Zwel1MZWSag2y6aWEkSWJf0T7WZ6/ncOlhss3ZBBoC8dJ4cbj0MFlVx/b0DPMI4/I2lzMldgpZBVncsP4Gbgy7kfEdxjNr6SyKlcX00ffhy2lfohAUZOdlcPPimyhVVPBe1zcZ2KN5InWmyc6qYOLHmwhSKfnw7v5sK6xkd24FiQVmskuqqSq3ItnF2vKSUkDnpSHAV08rk5EuIV4MiPDFX6dm3DvriW3j16jQ51fbWJBWTHJVDZvKzFgliVJJpFojwL9yvjyldnLPoAb/7o+Ls7SUyiVL2PvBu+yJdi80h7brQNzAIbTrN6jexuwyMucKstCfQSRJIrsqm4MlB0kuT2Zv4V62529HKSgRJREJiff6vMfI9iNxuVzc+9u9rLet59G4RxkQ1o/blt+KhWo+7PkuvbsOPNu30ySWrEjmzlVH6pyTVAJaLy3B/npiTEa6hHgzINyXnoGetVsX/puY55Yi2lw8Nr0zd3Zv3s5W/+ByiWxJyGDGwmQm6yv56L7G920FEG02LBs3UrFgIVWrVoHDgbZNG6wD+xN63bX4hEU03oiMzFlGttGfQQRBIMIrggivf6X1rUhnXtI8vov/DqVLSddgt0ukUqnk7cveZtSPo3jz8JtwGLzx4PO+n9C1Y/NmomeT8aNjGZRXyhrRzkA/Dx7o24oeAR5omxFQ9Y8Z58r2ISc9DqVSQen+IyjKXUyOa9iM9A/OsjIsmzZTtWollnXrEaurUXp74zfjarynTkXXrt1Jj0NG5lxDFvozQLR3NA/2epCR2pH88ucvOG3O2mt6rZ5PRnzCrJV3YsfBhNwxBBtDz+JoT46vr+rBhBUH2K2RCFUomyXyieXV7qjcKA/8dSefS72yuIw3txfjL6joO7h+CgNXeTmVS5dRPm8e1gMHQJJQ+vvjNXEinmPHYuzbB0Et53KXufCQhf4Molaq0Yj1E1V1jerK31P+YtuyTex2HmDO7DncfsfteAefXGKts0GN3cVMXx8esVUwc2cya8Z3a3Ld97elI0hwa6+TM9n8w/ezl5Ct8+XVOAFvf/dGLy6zGfOqVVQsXoxl8xa3WSYujoC77sI4cAD6Ll0QzuL+rjIyZwJZ6M8gdrvbE0SjqS/2/v4mJsyYQs0HFg6UJvHH7F+YesOV+IQ0bTekM4mtsoTi1LWUlW7HYkvmK2V/FqhH1V53SRKiS2xyDhe7w222uTTi1O515LCufPDzEb7eU0W/sg+xff5J7TVVaAh+11+H1/gJ6Dp2kFMQyFxUyEJ/BrHZbABotdrjlhk1fTyOHx0cKc/gg88+oqOpDcMmj8Q/MuhMDRNwLyo77JU4rRW4bDXYyyspy9xJSfUqzIb9oHAhuNRoHZG0V5hZAHhgY02njkSYmhdbMKNLKMs3ZfLE8sMsua7xLfuOR1jndvRam8nGYi0Llq5izNHzUT/9iL5bN1ncZS5aZKE/g5SVlQFgtVoxGhv2wfYO8uOqB2ZSlJLHuoWriC9K4uDXScT5RSNJEBhgYsS1LZNbB8BpraaqIAFzcRKWqhRqatKpFlOwarNB4apbWAFqTSBBXIEpaDT+0X1RGQ0MBCr2zOfz8mg+yVrBa6bLmjWG4ZH+eJkMpOdVNXv8WaXVrD5cyKrDhWxNKcHuEpnZI4hbH34PQ8D5Y/qSkTmdyEJ/GnA6neTk5ODv74+Hh3t2W11dzb59+9DpdKiaECJvah3CFfddS2lmEev+Xsm+Yrf74uHyNAZbR6HWnfymFKVp28lJ+Y0q2z5qtOl1BF0l+aMnEm9XX9T4o1Z6olDrUen0eEW3x9O/Y4Mz4+e6TiZj+3xml0czNGMj46Kal8/d5KsjNbW80XIuUWJPZhmrDheyOqGQIwXuh0NMgJHr+0cxqWso3SJ8mtW3jMyFjiz0LUR+fj7Lli2juLgYs9mMJEkoFArat2/PxIkT+fvvv7HZbNxyyy14e3s3uV2/SBOX3X01XeOT+e439zZ6h9bvpeuYpps4RFGkPGMn+amLKLWuw6bPQpDUGMS2BEtX4mnsiIdfGzyC2qLxOnEir+OhUCj4sMdYhm7ezEOpSjr55RLu2XTvoXZBnqQeKWVTThkDw+rmAHK6RLallbLoQB7LDuZTYrGjUgj0jvbj6YntGREXSEwzzUUyMhcTstC3EAkJCaSlpdGtWzc8PT0JDg4mOzub7du3k5KSgtVqZfTo0YSGnpzrZEzHWJ5p8zT/e/Utdu7c2ajQu2xWCo+soSh/GWXiZpyaEkDAKLUnWHU/ET1moPVp2YVeT42RTztEMC2+gmt372LRAC+M6qYJsOZoRKtDdAfwSZLEltQSFuzLZVl8AaUWO3q1khHtAxnXMZghbU1462VXSBmZpiALfQszZcqU2p87duxIhw4d2Lx5M4Ig0K/fiUP7G0OpUdGjdRc2puwg93AmoXF13RHt5jLy4hdTXLiCSu1ORFUNgqDB09GDAI87CO4wEb3f6V3U7RsYx2uV63goK4Lbti/iu35TUSobF+TdGWUIGgWdfY18tSGVH7dlklpswahRMrJ9EBM6hzC0rQm9RnaFlJFpLrLQn2YiIiKYPn16i7XXbWgvNqbsIGXvEULjIrFW5pMT/yfFpaswa93eMEqNF77iUEx+owlsNxq1/swm37omdijJ1cv5tKQdj+36lTd7XX3cVMIAlU4XWVY72EV6v7ISUYJeUb7cMzKW8Z1C0KllcZeRORVkoT/P8I8IxICWtQlbSHhnBa27/QGAhmCCnFMJip6Af5sBKJRn91f7bOfRZO1YzPeWjmj2zOfl7lPqiH2Zw8nsnGLmF5aTaLFCtAfedolrO4RwRfcw4oJPbq1ARkamPrLQtxD/eKJIknRa/bUFQeDScZM4su8wlbYEAJxOA/2HLcVg9Dxt/TYXQRD4vNd4bt7+F99UxuDYPY8Xul3GxnIzS4or+KuwnGqXyEAfDyZHB9Pb28jASR4oZV93GZkWRxb685B2/TrTrp97M/DdewIoK/sfq1Zfz9ixP6LR6M/y6I6hVCj4qvd4bt7yE3OrujN3wwEAvFQKLjH5cEeEifYe5854ZWQuVGShP8/p0X0Wm7fkAj+xctUQjMbRdGh/MyZT67M6LrM5keycuRQWLuE6RxmeimtxeQ7l6la96edjRHMObqQiI3OhIgt9C/FPHpuzwYD+L7N3bxtKSl/E6fyF3XuKGDvmyzM+DkmSKC3dSFbWN5SUrkeh0GIyjSEocCLD/YagVB4/9YOMjMzpQxb6FmDHjh1s2bKFyMhTy754KnTpch1Ll72PIDjo3+/ZM9q31ZpHUdFycnJ/xmJJRKMxERPzIGGhV6PRyGkIZGTONrLQnyK//PILCQnuRdFx48adtcRZJSXJaLUV2KwRaDSn353S5bJSWLiInNyfqajYDYCHRwc6tH+LoKCJKBTy7F1G5lxBFvpTQBRFUlJSao8rKytPOvL1VPH3j8Xl7IdWt5Xk5PV06TKlxftwOi2Ulm2kuGgVRcUrcTorMBha0zrmIUymsRiNZ3ddQEZGpmFkoT8FFAoFjz/+OPv27eOvv/7C6yTzxLTUWPSGIOx2yM55kaLiNYwc8f4pt+tyWSkqWk5+wd+UlW1CFO2oVF4E+A8nJHQavj795PS/MjLnOLLQnyIKhYJWrVqhUqnYtGkT06ZNO2tj6dP7ebZt0wM/43QuO6W2qqvTyMyaTUHBXzidVeh0YYSFXUNAwEh8vHuhUMh5ZmRkzhdkoW8BfHx8GDhwIOvWrWPAgAGEhYWdlXFotV5YbfkolRAa+mKz6kqSRH7+fLJz5lJZuQ8AhUJDoGk8ISFX4OvbD0GQXSJlZM5Hmiz0giAogZ1AjiRJkwRB8AN+AaKBdOBKSZLKGqh3H3ArIABfSpL03qkP+9yjf//+bNy4kb179541od+//yeUyrU4nUPp3OnKJtWpqckhv2A+BQULsFiSas9rNIH06bMArSbgNI1WRkbmTNGcGf19QALwjyH6cWCVJEmvC4Lw+NHjx/5dQRCETrhFvg9gB5YKgrBIkqQkLjB0Oh2dO3dm586dRERE0KVLlzPaf3V1GUXFTwPQscNdmM1FbN/xFh3azyQ4uEOdsk6nhaKipeTlzaOsfCsA3t69aNvmWQIDx6HRBMp2dxmZC4gmCb0gCOHAROAV4MGjpy8Fhh39eQ6wlv8IPdAe2CpJUvXRdtYBlwFvnsqgz1XGjRtHWVkZ8+bNY/PmzUyZMoWgoKAzIpoGgy9wOfAHRxKPzeb37E1i/Lg/kSSRsrKt5OXPo6hoGS5XNXp9JK1a3U9I8BT0+ojTPkYZGZmzgyBJUuOFBOF34DXAE3j4qOmmXJIkn3+VKZMkyfc/9doDfwH9gRpgFbBTkqR7GujjNuA2gMjIyJ4ZGRknfVNnE5fLxe+//17rWx8YGEiPHj3o1q0bOp3utPe/Z8/XlJa9WudcRPhNFBUtxWrLRan0IChoIiHBU/H27inP3GVkLhAEQdglSVKvBq81JvSCIEwCJkiSNEsQhGE0Q+iPnr8ZuAswA4eAGkmSHjhRn7169ZJ27tx54rs6x7FYLBw6dIi9e/eSk5ODWq2mXbt2xMbG4uvri8FgQK/XYzAYTpirvSFqairYv/9b9PogWrceRWbmRpRKPenpv6FUra1XXhDU+PkNIDj4MkwBo1EqT/8DR0ZG5sxyqkL/GnAd4AR0uG3084DewDBJkvIEQQgB1kqS1K6Rtl4FsiVJ+uRE5S4Eof83ubm57Nixg8TERCwWS51r0dHRzJw5s1ntLVo8HJ0us9Fyvj793OJuGoNaLed3l5G5kDmR0Ddqo5ck6QngiaMNDcM9o79WEIS3gBuA149+/+s4nQdKklQoCEIkMBW3GeeiIjQ0lEsvvRRRFCkqKqKqqoqamhr++OMP1Orm+6OrlBFAw0Kv10cREjyV4ODL0OvPjvePjIzMucWp+NG/Dvx61DSTCUwDEAQhFPhKkqQJR8v9IQiCP+AA7mrIBfNiQaFQEBQURFCQe9/WdevWoVQ2b5s8p9NJmzbPs2PHIgJM7wGg18cQFDQeU8BoPD07yXZ3GRmZOjRL6CVJWovbuwZJkkqAkQ2UyQUm/Ot48CmN8AImKCiI+Ph4Nm3axIABA44r0KIokpmZyYEDB4iPj8dqtaJUKhGEVxk4sAdhYW3O8MhlZGTOJ+TI2LNI9+7dyc7OZsWKFRQUFDB58mRUKvevxOl0kpKSQkJCAomJiVRXV6NWq4mLi6Njx460atUKrVbOECkjI9M4stCfRWJjY7n//vtZv349a9aswWq1MmTIEPbt28fBgwepqalBp9PRpk0b4uLiiI2NlcVdRkam2chCf5YRBIGhQ4eiUChYtWoViYmJqFQq/t/e/YTGUcZhHP8+IhEqFG1r0taWFkINRiUoQg4qgmgxl0CRHsSDJaceRC+CDSnxUASreJGCRTx4KSIo0haR+ge9SME/GNsqaeMh2Iqt/w4ShYr4epg3mK6zm83O7szk7fOBYd7OOxN+T1J+O/vuJjs0NMTIyAiDg4MrXsc3M1vKjb4mRkdHWVhYYGBggOHh4VJ+ucrMrg5u9DXR19fH2NhY1WWYWYL8d2fNzBLnRm9mljg3ejOzxLnRm5klzo3ezCxxbvRmZolzozczS5wbvZlZ4tr6KMGySfoZqONnCW4Afqm6iC5ynvpKKQs4Txm2hRBuypuoZaOvK0lfNPsEl9XIeeorpSzgPFXz0o2ZWeLc6M3MEudGvzKvVl1AlzlPfaWUBZynUl6jNzNLnO/ozcwS50ZvZpY4N/oGkt6UNBO3eUkz8fhDkr6UdDruH1jm6zwtKUjaUErhzesolEfSAUmn4vXvS9pcaoArayma5UVJszHPO5JuKLP+nHqK5tkt6RtJ/0iq/K1+XcizTtIHkubi/sZSA1xZS7Ms6yV9LGlB0qEW149IOhkzH5e0trTi84QQvDXZgJeA6Ti+E9gcx7cDP7S4bitwguyXvjZUnaNIHmDtkvGTwOGqcxTIshO4No4PAgerzlEwz63AEPAJcHfVGbqQ5wVgXxzvq8vPpyHL9cC9wF7gUItrPgfuj+MJ4EClGar+JtZ1AwScB3Y0mfsVuK7JtW8BI8B8XRp9kTxLzpsEXkkkyy7gSNVZupGnbo2+0zzAWWBTHG8CztY1C7BnmUb/O/+92WUr8G2VObx009x9wKUQwlzO3CPAVyGEy40TksbJ7li+7nWBK9RRHgBJz0k6DzwGTPewxnZ1nGWJCeC9rlfWmW7kqZNO8wyEEH4EiPv+HtbYrlZZWjkDjMfxbrJmX5mr8sPBJX0IbMyZmgohHI3jR4E3cq69jexp/86cuTXAVN5cL/Uqz6IQwhQwJWkSeAJ4tnDRTfQ6SzxvCvgbOFKs2uWVkadMKeUpkqUNE8DLkqaBY8BfnVXZJVU/NarjRvYAeAnY0nB8C3AOuKfJdXcAP5Et2cyTNZPvgY2rMU/O19kGnFnNWYDHgZPAmqr/n3XrZ0ONlm6K5KFmSzfNssS5PbRYumk49xbgsyqzeOkm34PAbAjhwuKB+A6Nd4HJEMKneReFEE6HEPpDCNtDCNuBC8BdIYSLJdTcSkd54nk7lvxzHJjtVZFtKpLlYeAZYDyE8GevC21Tx3lqqkieY2QPxMT90RbnluF/WdolqT/urwH2A4e7XNvKVPkoU9cNeB3Y23BsP/AHMLNk649zr5FzR0VNXowtkgd4m2y98RRwHLh5FWf5juyFtcVzKn8HUcE8u8huJi6T3XmeWOV51gMfAXNxv65uWeLxeeA3YCF+/4dzsjxF9gzmHPA88YXZqjb/CQQzs8R56cbMLHFu9GZmiXOjNzNLnBu9mVni3OjNzBLnRm9mljg3ejOzxP0Lsh8/1/7bDl0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  50731.0 people (VAP of 41711.0 ) and  30803.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first downstate tract is  0\n"
     ]
    }
   ],
   "source": [
    "#ok, what's the first downstate tract?\n",
    "firstTract = -1\n",
    "for t in range(nTracts):\n",
    "    if isDownstateTract[t] == 1:\n",
    "        if firstTract == -1 :\n",
    "            print(\"first downstate tract is \",t)\n",
    "            firstTract = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5ed3aeb1-bd4e-4282-83d1-6af29983db59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 50\n",
      "working on tract 100\n",
      "working on tract 150\n",
      "working on tract 200\n",
      "working on tract 250\n",
      "working on tract 300\n",
      "working on tract 350\n",
      "working on tract 400\n",
      "working on tract 450\n",
      "working on tract 500\n",
      "working on tract 550\n",
      "working on tract 600\n",
      "working on tract 650\n",
      "working on tract 700\n",
      "working on tract 750\n",
      "working on tract 800\n",
      "working on tract 850\n",
      "working on tract 900\n",
      "working on tract 950\n",
      "working on tract 1000\n",
      "working on tract 1050\n",
      "working on tract 1100\n",
      "working on tract 1150\n",
      "working on tract 1200\n",
      "working on tract 1250\n",
      "working on tract 1300\n",
      "working on tract 1350\n",
      "working on tract 1400\n",
      "working on tract 1450\n",
      "working on tract 1500\n",
      "working on tract 1550\n",
      "working on tract 1600\n",
      "working on tract 1650\n",
      "working on tract 1700\n",
      "working on tract 1750\n",
      "working on tract 1800\n",
      "working on tract 1850\n",
      "working on tract 1900\n",
      "working on tract 1950\n",
      "working on tract 2000\n",
      "working on tract 2050\n",
      "working on tract 2100\n",
      "working on tract 2150\n",
      "working on tract 2200\n",
      "working on tract 2250\n",
      "working on tract 2300\n",
      "working on tract 2350\n",
      "working on tract 2400\n",
      "working on tract 2450\n",
      "working on tract 2500\n",
      "working on tract 2550\n",
      "working on tract 2600\n",
      "working on tract 2650\n",
      "working on tract 2700\n",
      "working on tract 2750\n",
      "working on tract 2800\n",
      "working on tract 2850\n",
      "working on tract 2900\n",
      "working on tract 2950\n",
      "working on tract 3000\n",
      "working on tract 3050\n",
      "working on tract 3100\n",
      "working on tract 3150\n",
      "working on tract 3200\n",
      "working on tract 3250\n",
      "working on tract 3300\n",
      "working on tract 3350\n",
      "working on tract 3400\n",
      "working on tract 3450\n",
      "working on tract 3500\n",
      "working on tract 3550\n",
      "working on tract 3600\n",
      "working on tract 3650\n",
      "working on tract 3700\n",
      "working on tract 3750\n",
      "working on tract 3800\n",
      "working on tract 3850\n",
      "working on tract 3900\n",
      "working on tract 3950\n",
      "working on tract 4000\n",
      "working on tract 4050\n",
      "working on tract 4100\n",
      "working on tract 4150\n",
      "working on tract 4200\n",
      "working on tract 4250\n",
      "working on tract 4300\n",
      "working on tract 4350\n",
      "working on tract 4400\n",
      "working on tract 4450\n",
      "working on tract 4500\n",
      "working on tract 4550\n",
      "working on tract 4600\n",
      "working on tract 4650\n",
      "working on tract 4700\n",
      "working on tract 4750\n",
      "working on tract 4800\n",
      "working on tract 4850\n",
      "working on tract 4900\n",
      "working on tract 4950\n",
      "working on tract 5000\n",
      "working on tract 5050\n",
      "working on tract 5100\n",
      "working on tract 5150\n",
      "working on tract 5200\n",
      "working on tract 5250\n",
      "working on tract 5300\n",
      "working on tract 5350\n",
      "working on tract 5400\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, build the downstate NY map\n",
    "#build the initial (uncut) state map out of the TRACTS, excluding the clipPoly and lakeshore tracts\n",
    "tractMAP = tractGeom[firstTract]\n",
    "for t in range(firstTract+1,nTracts):\n",
    "    if t%100 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0 and isDownstateTract[t] ==1:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "23946ce5-7a30-48f3-a233-47c23b7b4c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's add a healPoly to smooth out NE border\n",
    "pt1 = Point(-74.05,40.66)\n",
    "pt2 = Point(-74.03,40.68)\n",
    "pt3 = Point(-74.01,40.74)\n",
    "pt4 = Point(-74.05,40.7)\n",
    "healPoly = Polygon([pt1,pt2,pt3,pt4])  #plan to clip off NE corner near Duluth\n",
    "plotPoly(healPoly)\n",
    "#OK, apply the heal-poly patch\n",
    "tractMAP = tractMAP.union(healPoly)\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ad14ca84-d3ba-47a5-a4d3-47dd4a9e305c",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'avgDistricPop' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_119/2824040129.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0mnDistricts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0mavgDistrictPop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstatePop\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnDistricts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I have saved whole-state data. NYC-LI pop = \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstatePop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mavgDistricPop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"(per district), with pctR of\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstateGOP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'avgDistricPop' is not defined"
     ]
    }
   ],
   "source": [
    "#Now, let's create a csv that identifies whether tracts and precincts are upstate or downstate\n",
    "#then, zero out the tractPop and vtdBiden, vtdTrump of the upstate areas\n",
    "tractNo = [0]*nTracts\n",
    "precinctNo = [0]*nPrecincts\n",
    "for t in range(nTracts):\n",
    "    tractNo[t] = t\n",
    "df = pd.DataFrame( {\"tractNo\": tractNo,\"isDownstate\":isDownstateTract,\"tractPop\":tractPop,\n",
    "                    \"tractVAP\":tractVAP,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack} ) \n",
    "\n",
    "outname = \"NYC-LI_tract_input.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "for t in range(nTracts):\n",
    "    if isDownstateTract[t] == 0 : #zero these out for this NYC-LI submap\n",
    "        tractPop[t] = 0.\n",
    "        tractVAP[t] = 0.\n",
    "        tractHisp[t] = 0.\n",
    "        tractBlack[t] = 0.\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[t] = p\n",
    "df2 = pd.DataFrame( {\"precinctNo\": precinctNo,\"isDownstate\":isDownstatePrecinct,\n",
    "                    \"vtdTrump\":vtdTrump,\"vtdBiden\":vtdBiden} ) \n",
    "\n",
    "outname = \"NYC-LI_precinct_input.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df2.to_csv(outpath)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    if isDownstatePrecinct[p] == 0:  #zero these out for this NYC-LI submap\n",
    "        vtdTrump[p] = 0\n",
    "        vtdBiden[p] = 0\n",
    "\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump)+np.sum(vtdBiden) )\n",
    "statePop = np.sum(tractPop)\n",
    "nDistricts = 16\n",
    "avgDistrictPop = statePop/nDistricts\n",
    "print(\"I have saved whole-state data. NYC-LI pop = \",statePop,avgDistrictPop,\"(per district), with pctR of\",stateGOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b0dad403-012b-4bdd-aaae-b4821a1e5942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have saved whole-state data. NYC-LI pop =  12429978 776873.625 (per district), with pctR of 0.3101675952046603\n"
     ]
    }
   ],
   "source": [
    "print(\"I have saved whole-state data. NYC-LI pop = \",statePop,avgDistrictPop,\"(per district), with pctR of\",stateGOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?  #NYC-LI only\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0 0\n",
      "1000 0.0 0\n",
      "2000 0.0 0\n",
      "3000 0.0 0\n",
      "4000 0.5684556407447974 913\n",
      "5000 0.23613312202852615 631\n",
      "6000 0.09673024523160763 734\n",
      "7000 0.029411764705882353 442\n",
      "8000 0.15535714285714286 560\n",
      "9000 0.1323943661971831 355\n",
      "10000 0.2995169082125604 414\n",
      "11000 0.0 0\n",
      "12000 0.0 0\n",
      "13000 0.5219594594594594 592\n",
      "14000 0.0 0\n",
      "15000 0.5114155251141552 438\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0 and isDownstatePrecinct[p]==1: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%1000 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 0.54448547616672 0.54448547616672\n"
     ]
    },
    {
     "data": {
      "image/png": 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eBYwCUEq1BYZlsVAAHAK8lFKVgZvAK4Dl9YhnRUIehABQ2slyjr6c7J1wLuRMaHSo0VFEPhOXGMetyFsERgRyM+ImgRGByY8jkx+fCjlFRGwEAOWLlmdw08H0rduX+uXqG34vw6ITi7jz4A7/5/N/huYwlRwP1aSUegH4L1AaWK+UOqa1flYpVR6YrbXuqrVOUEp9BGwGbIG5WutTJkleQN2NvgtgUUcWsQmxxMTHmHT4SVGw7b+xn692fcXmi5vR/2i5LmJfBHcXdyq4VOC1Oq/hU96HRuUbUbdM3TzpNTarfMr7ULxwcQZvHEyVElXShh2wVjIGt5UZumkos4/OJuqzKKOjPOKtNW+x+ORiTrx3ghquNYyOky8kJiVyLvQchWwLUa1kNaPj5Ik91/fw5c4v2Xp5K65OrrzT4B2ql6pOhaIV0gpEMYdihh81ZNWp4FN0XdyVu9F3WdxrMT1rmu9iUHOPwS3Fwso8u/BZQh6EcHSg5ZzgBrgddZtaP9TiqbJP8Xf/v/P0Ovb8ID4xntMhpzkadJSjQUc5EnSE43eOEx0fDUBN15rUdK1Jw3INaejWkEblG1HOuZzBqR91J+oO686vY/vV7ZRyLIV3aW/qlKmDdxlvihcu/tj5EpMS2XltJ+N2jePvq39TpkgZhrcYzvs+71OkUJG8+wBmcjvqNj2W9ODwrcNM6TSFj5t9bJZiZ+5iYV0jhgsCggN4psozRsf4l3LO5ZjScQrvrn2X2Udn49vI1+hIVuGvS38xevtoTtw5QWxiLABFCxWlgVsDfBv60qh8I+5G32XblW2cCTnDmrNr0ppl3JzdaFS+EY3cGqXdPZ+QlEBCUgKJSYlpjxOSEkjU/3semxBL8INg7jy4Q7HCxahWohrVSib/1HStSVGHrF0hHx0fzYXQC6y/sJ6159dyIPAAGk0553JExUURFfe/o9/yRcv/r3iU9sbdxZ0jQUfYfX03e6/vJTIuknLO5ZjaaSoDfQZa1IiQuVXOuRw73tzBG6ve4JMtn7A/cD+/9vwV50LORkfLFjmysCL3Yu5RalIpJj0zieEthxsd51+01rT/rT3+Qf6c+fAMbkXdjI5k8YZvGc6U/VP4T7P/pLW9VytZ7bFHZpGxkRy7fYwjQUfSjkDO3j1Lkk7K8joVClcnV8oUKUP4w3BuRt585P1KxSpRp0wd6papSw3XGjyIe8DNyJvcirz1v98RN7kfez9tHp/yPvSo3oPnajxHvbL10Ghu3L9BQHAAp0JOJf8En+J0yGliEmLS5vMu7U0rj1a0rtSa52s+j6O9Yza3oPVI0kn0W9mPJQFL+Ov1v0z+pU+OLESak3dOAlC3bF2Dk2RMKcWs7rOoO7MugzYOYkWfFUZHsnip5yKGNBuCR7HMb0Yt6lCUVpVa0apSq7TXHsQ94Pr969ja2GJnY4etSv6d+pP6eup7tja2jxSj6PhoroRd4cK9C5wOOU1AcAABwQFsubSF+KR4AGyVLW5F3ahQtAI1StWgvWd7KrhUoKJLRdpVbkf5ouUfyalQVCpeiUrFK9Gtere01xOTErkafpXr969Tt2xdi7pQw9xslA2ftfqMJQFLOHv3bFqx0FpbxTkYKRZW5GRwSrEoY5nFAsCrlBdftPmCz7Z/xuqzq3m+5vOG5NBaExIdQiHbQhS2K4yDrYNF/of0KuUFwIXQC1kqFhkpUqhI2uiKOeFk74R3GW+8y3g/8u8VnxjP1fCrFHUoSpkiZUxyHsrWxpaqJatStWTBvNu/Tpk61C9Xnzn+c/iw8YeM3DqSyfsmc+6jcxbfEaechbQiJ++cpEThEv/6FmdphrUYxlNln+LDDR9y/+H9zGcwg3G7xlF2SllKTCyB43hHbL6yofC4wjT8uWHa5ceWIPUPxIV7FwxO8m/2tvZ4lfKinHM5uWDBhHwb+nLs9jFaz2vN5H2TATh2+5ixobJA9gArEhASQN2ydS3yG3J69rb2/PLcLwRFBjFq2yhDs4xrN44JHSbwRZsv+KjJR5wOOU3flX1JTEo0NFeq8kXL42jnyIVQyysWwjz6PdUP50LO+AX6Mb79eCoUrcCXO78kPjHe6GhPJM1QVkJrTUBwAP3qWv643ABNKjRhSNMhTDswjb51+5p8oJno+GgcbB0eexNWTdeaQPIofvXL1U97vXqp6gxcN5Dxu8fzeZvPTZopJ2yUDdVKVuP8vfNGRxF5pKhDUTb13USRQkWoX64+XiW96LOiD6vPrqa3d2+j4z2WHFlYiev3rxMRG2GxJ7cz8nX7r6lUrBID1g4gNiHWJMuMT4zn651fU+zbYtT8oSZzjs4hLjHuX9Ol3hh47u6jvegPaDiA1596nbE7xrL72m6TZMotr1JecmRRwLT0aJn2JcbdxR3A4ntAkGJhJQKCAwDLPrn9T86FnJnZbSZn7p7he7/vc72886HnaTG3BZ/v+Jznqj9H0UJFeXftu1SdUZXpftN5EPcgbVqvkl4oFGfvnn1kGUopZnabScViFRmyaUi2Ljk1F6+SXlwOu0xCUoLRUYQB/AL9ACx+YCQpFlbiXGjyN+TcXPVihC5eXahcvDLH7xzP9bL6rezH5bDL/P7S76x8eSVHfI+wse9GKhevzNDNQ/Gc7sn4XeMJfxiOo70jlYpXSttu6RUpVIRvO3yL/21/fvX/Nde5cqt6qerEJ8Vz/f51o6MIA4Q/DAfg0r1LxgbJhBQLK3E+9DylHEtR0rGk0VGy7X7sfUo5lsrdMh7e5/CtwwxuMjitXVcpRedqndn11i52vbmLxuUbM+bvMXhO8+TY7WNUL1X9sVcZvVLnFVp5tGLYX8O4FWlsr/leJf93+awoeD5o/AFeJb3otrgb/kH+Rsd5LCkWVuJ86HmLvw47I0k6ibCYsFwXub039qLRtK7UOsP3W1VqxYa+GzjiewQHOwd81/ri4eLB1fCrGU6vlGJOjzk8THjIe+veM3S42tR7Lc6Hyknugqisc1m2vrEVR3tH+q/ub3Scx5JiYSWstViEPwxHo3N9ZLHr2i7sbexp6p7hqLxpGro1ZGqnqRy6dYitV7ZyN/ruI30UpedVyotx7cax9vxafNf6cirYmN7zHe0csbext8h7LUTecHN2Iz4xntqlaxsd5bHk0lkrEBUXxc3Im1ZZLFIHRMrtkcWua7toXKFxljqYe63ua4zbPS7t5Pa18Gt4l/HOcNqhzYZy4d4F5h2bx2z/2XiX9qZY4WJpd3072DngYOuQ9rxxhca83eBtk9ykprVm5ZmVDN40mISkBKu6eEGY1m/HfyM0JpR+T1nupfFyZGEFLt67CGCVxeJezD0ASjnl/MjiyK0jHL51mFYerTKfmOQmpuKFi+Pi4ALw2KYoSO5+4qfuPxH4n0Amd5xM5RKVcbJ3Ii4xjrvRd7kcdhn/2/7suLqDlWdXMmDtAJ6e+zSnQ07n+PMA3Lh/g55Le/LS8pco7VSaA+8eYECjAblaprA+cYlxtJnXhnfXvotPeR+6eXXLfCaDyJGFFUhty7bGYhEak3xkkZNmqDMhZ/h/f/8//jjzByUdS9K3bt8sz2tnY0dFl4qcCjnFlfArmU7v6uTKsBbDGNZi2GOn0Vqz8MRChm4eSv2f6jO61Wg+ffpTHOwcspwrMSmR/x78L2O2j0GjmdxxMkObDcXORv4rFkR+gX7suraLgY0G8mXbLy26dwY5srACp0NOo1BWOVpaTpuhVp9dTYOfG7D50ma+aPMFlwdfztYNibbKlpKOJSlsV9hkJ46VUrxe73XOfHiG3t69GbtzLA1nNWT/jf1Zmt8/yJ+ms5vy8eaPaV2pNac+OMWwFsOkUBRgywKWoVCMbz+ess5ljY7zRFIsrID/bX9quta0ygFhctIMNe/YPF78/UXql6vPpcGXGNt2LMUKF8vWem1tbNl9fTcPEx5Sp0ydbM2bmTJFyrCo1yI2vLaBqLgoWs5tyaANg4iMjcxw+qi4KD7Z/Ak+v/gQGBHI0heXsv619XgW9zRpLmFdzt09x8zDMxnYaGCummnzihQLK3A06KjVDvYeGhOKQlHM4d9/7CNjI5m4ZyL1fqrHyjMrAZi6fypvrXmLDpU7sPWNrZQpUiZH6z1x5wQA7zR4hwENzXMuoItXF059cIrBTQfzw6EfqP1jbY7ffvTmw/Xn1+P9ozdT/abyboN3OfPhGV6u87JFNzeIvDFi6wiUUgxqOsjoKFkix78WLvhBMIERgVZbLO7F3KOEY4lHOvwLfxjOfw/8l2kHpnEv5h5li5Tlpd9fwtXJlZDoEHrX7s2CFxZk61zAPw1rPoyTwSeZ2W2mWf8wOxdyZlrnabxa51Ve/P1Fei7tyb539qFQDNk0hOWnl1O7dG12v7Wbpz2eNlsOYV0CggP489yfjGs3zqIvl01PioWFS72j01qLRWhM6CPnKxadWJQ8zkXsfZ6r/hxjWo+hTpk6NPy5IedCzzGw0UB+6PrDY3uTzaqRT4/MbfRsaerelFUvr6L1vNZUm1ENe1t7YhNiGdduHMNbDqeQbaE8zSMsW+oVjl28uhicJOukWFi4o0FHAR7pZtuahEaHUsqxFAlJCYz8ayRT/abSyqMV0ztPp4Fbg7Tp/nz1T/bd2Ef/ev2ttommcYXGnHjvBBP2TCAqLorx7cen3Z0tRHqpXx4+//tzVr+y2ioucshyQqWULXAYuKm17q6UKgksAzyBq0AfrXVYBvMNAQYACvhFaz0t97ELjqO3j1K1RFWKFy5udJQcuRdzD3tbezov7My2K9sY1GQQ33X67l/dMVcvVd0qLw3+J69SXsztOdfoGMLC1SiV3IX++gvrOR963iqaorJzgnsIcCbd80+BbVprL2BbyvNHKKXqkFwomgD1gO5KKfmqlQ3WfHIbkpuh/AL92HN9D7/2/JUZXWZYfL/9QpjTzYibvLDsBexs7Pjt+d+solBAFouFUsod6AbMTvdyT2B+yuP5wPMZzFoL8NNaR2utE4CdwAs5TlvAhD8M53LYZRqUa5D5xBYqNiGWCkUrsPut3bxZ/02j4whhqCthV2g+pzlXw6+y4bUNvF7vdaMjZVlWm6GmASOAouleK6u1DgLQWgcppTK6xjEAGK+UKgXEAF1Jbsr6F6WUL+AL4OHhkcVY+VvqIO7WfGSx5fUtuDm7WcV15EKY27Hbx7gRcYOWFVta3XnITI8slFLdgWCt9ZHsLlxrfQaYCPwFbAKOAxkOB6a1nqW19tFa+5QuXTq7q8qXUk9upz8RbG3qlKkjhUKIFC/UeoGfu//MgZsHKDOlDL2W9TI6UpZlpRmqJdBDKXUVWAq0V0otBO4opdwAUn4HZzSz1nqO1rqh1ro1cA+Qfpiz6GjQUdxd3HN8Y5oQwvL4NvLl+HvJN2+uPb/W4DRZl2mx0FqP0lq7a609gVeA7VrrfsCfQOpIHf2BNRnNn9o8pZTyAHoBS0yQO9/TWrM/cD+N3BoZHUUIYWK1S9dmZMuRJCQlpPWfZuly093Ht0BHpdQFoGPKc5RS5ZVSG9JN94dS6jSwFvgwo8trxb+dDz3P5bDLPFv1WaOjCCHMoE2lNgBM2jvJ4CRZk607QbTWO4AdKY9DgQ4ZTHOL5BPZqc+zNgiBeMS68+sA6Fbdcvu3F0LkXBevLrxa51V+OPQDw1sOx9XJ1ehITyQdCVqodRfW8VTZp/AoJleGCZFfjWk9hgfxD1hy0vJb56VYWKDwh+Hsvrab7l7djY4ihDCjWq61sFE23I66bXSUTFl+hyQF0OaLm0nUiXSvLsVCiPwqISmBr3Z+RZJOsoqOJqVYWKB1F9bh6uRKkwpNjI4ihDCDS/cu0W9VP/wC/Xiz/ptPHM7XUkixsDCJSYlsuLCBbl7dct1NtxDCsmitWXBiAR9u+BBbZcuyl5bRx7uP0bGyRIqFhfEL9ONezD1pghIinwl+EMzAdQNZfXY1rTxasbDXQqu6gEWKhYVZf2E9djZ2dKrayegoQggT+eP0H7y3/j0iYyOZ0nEKQ5sNtbqWAykWFmbd+XW08mhlteNXCCH+517MPQZtHMTik4tp5NaI316wni7J/0kunbUg18KvcTL4pDRBCZEPbLiwgTo/1uH3U7/zVduv2P/OfqstFCBHFhZlw4XkXlKkWAhhvSJiI/hk8yfM9p9NnTJ1WP/aeqvuOTqVFAsLsvv6bioUrZAvhhcVoiD6+8rfvLXmLW5E3ODTlp8ytu1YHOwcjI5lElIsLIhfoB/NKzY3OoYQIpui46MZtXUUMw7OwKukF3ve2pPv/i/LOQsLcSfqDlfCr9CsQjOjowghsmH/jf3U/6k+Mw7OYFCTQfgP9M93hQLkyMJiHLh5AIBm7lIshLAGsQmxjN0xlkn7JuHu4s62N7bRvnJ7o2OZjRQLC+EX6IedjZ1Vj7ctREHhH+TPG6vfICA4gHcavMPUZ6fi4uBidCyzkmJhIfwC/ahfrj6O9o5GRxFCPEZYTBjf7P6GaQemUdqpNOteXVdgxpyRYmEBEpMSOXTrEG/We9PoKEKIDMQlxvHjoR/5etfXhMWE8Wb9N5nSaQolHUsaHS3PSLGwALuu7SIqLorWlVobHUUIkY7WmtVnVzP8r+FcCrvEM1WeYUrHKdQrV8/oaHlOioUFWHxyMc6FnAvM4awQ1uDivYsM2jiITRc34V3am419N/Js1WdRShkdzRBSLAwWmxDLijMreKHmCzjZOxkdR4gCLyY+hm/3fMvEvRMpZFuIqZ2mMqjpIOxsCvafy4L96S3AhgsbCH8YTt+6fY2OIkSBt+78OgZvHMyV8Cu8WudVpnSaQvmi5Y2OZRGkWBhsccBiyhQpQ4cqHYyOIkSBdTX8KkM2DeHPc39Sy7UW29/YTrvK7YyOZVGkWBjo/sP7rD23Ft9GvgX+EFcII8QmxDJl3xTG7x6PjbJh4jMTGdpsqFWMiZ3XsvwXSillCxwGbmqtuyulSgLLAE/gKtBHax2WwXwfA+8CGjgJvKW1fpj76NZv5ZmVxCbGShOUEHlMa8268+v4ZMsnXLh3gZdqv8TUTlOpWKyi0dEsVnb6hhoCnEn3/FNgm9baC9iW8vwRSqkKwGDAR2tdB7AFXsl53PxlccBiqpaoSpMKTYyOIkSBceLOCTou6EiPpT2wUTZs6ruJ5b2XS6HIRJaKhVLKHegGzE73ck9gfsrj+cDzj5ndDnBUStkBTsCtHCXNZ4Iig9h+ZTuv1X2twF6KJ0ReCn4QzMC1A2nwcwOOBh1lRucZnHz/JM9We9boaFYhq81Q04ARQNF0r5XVWgcBaK2DlFJl/jmT1vqmUmoKcB2IAbZorbfkLnL+sOzUMpJ0Eq/Vfc3oKELka7EJsUw/MJ3xu8cTHR/NoCaD+LzN5wXq7mtTyPTIQinVHQjWWh/J7sKVUiVIPgKpDJQHiiil+j1mWl+l1GGl1OGQkJDsrsrqLDq5iIZuDanpWtPoKELkS1pr/jj9B7V/rM3IrSNpXak1Ae8HMK3zNCkUOZCVZqiWQA+l1FVgKdBeKbUQuKOUcgNI+R2cwbzPAFe01iFa63hgJdAio5VorWdprX201j6lS5fOwUexHudDz3P41mE5sS2EmRwNOkrb+W15aflLONk7saXfFta+upYarjWMjma1Mi0WWutRWmt3rbUnySent2ut+wF/Av1TJusPrMlg9utAM6WUk0pumO/AoyfJC6TFJxejULzs/bLRUYTIVwIjAnlz9Zv4zPLhdMhpZnabif9AfzpW7Wh0NKuXm4v7vwV+V0q9Q3JR6A2glCoPzNZad9VaH1BKrQCOAgmAPzArl5mtmtaaRScX0a5yOyq4VDA6jhD5QkRsBBP3TGSq31SSdBKfNP+EMa3HUKxwMaOj5RvZKhZa6x3AjpTHoSQfKfxzmltA13TPvwC+yE3I/OTQrUNcvHeRUU+PMjqKEFYvPjGeX47+wtgdYwmJDuG1uq8xvv14PIt7Gh0t35HbhvPY4pOLKWRbiF61ehkdRQirpbXmz3N/MnLrSM6FnqNNpTZM6TQFn/I+RkfLt6RY5KGEpASWBiyle/XuFC9c3Og4QlilQzcPMeyvYey6tosapWqw5pU1PFf9OblfycykWOSh7Ve2c+fBHbkKSogcuBp+ldHbR7P45GJKO5Xmx64/8m7Dd7G3tTc6WoEgxSIPLT65mGIOxejq1TXziYUQAIQ/DOeb3d8w/cB0bJQNo1uNZkTLEbg4uBgdrUCRYpFHYuJjWHlmJb1r96awXWGj4whh8eIS45h5aCZf7fqKsJgw3qj3BuPaj8Pdxd3oaAWSFIs8su78OiLjIun7lDRBCfEkwQ+CWRqwlBkHZqSNez2542Tql6tvdLQCTYpFHll0chFuzm60qdTG6ChCWCStNbOPzuY/W/5DVFwUjdwaFfhxry2JFIs8EBYTxoYLGxjUZBC2NrZGxxHC4gRGBDJg7QA2XdxEO892zOgygzpl6hgdS6QjxSIP+AX6EZ8UT8+aPY2OIoRFuf/wPpP3TeZ7v+8B+L8u/8f7jd/HRmVnqB2RF6RY5IHr968DULVEVYOTCGEZYhNi+enwT3y962tCY0J5pc4rfNP+GyqXqGx0NPEYUizywLX717CzsaOcczmjowhhqCSdxJKTSxjz9xiuhl+lQ+UOTHxmIo3KNzI6msiEFIs8cO3+NSq6VJTzFaJA23JpCyO3juTY7WPUL1efzf0206lqJ6NjiSySYpEHroVfw6OYh9ExhDDEkVtH+HTbp2y9vBXP4p4sfGEhr9Z9Vc5LWBkpFnng+v3rtKvczugYQuSpezH3GPHXCOb4z6GUYymmPTuN93zew8HOwehoIgekWJhZfGI8NyNv4uEiRxaiYEgds+U/m//DvZh7DGs+TMaWyAekWJjZzcibJOkkKhWvZHQUIczu4r2LvL/+fbZe3krTCk356/W/qFeuntGxhAlIsTCz1MtmKxWTYiHyr7jEOCbvnczXu77Gwc6BH7r+wMBGA+WijnxEioWZ7bi6AwCvUl7GBhHCTPZc38PAdQM5HXKa3rV7M63zNMoXLW90LGFiUizMKCI2gml+0+hRo4cM8yjynbCYMD7d+imzjs7Co5gHa19dS/fq3Y2OJcxEioUZ/XDwB8IehvH/Wv8/o6MIYVIrz6zkg/UfcDf6Lp80/4SxbcfiXMjZ6FjCjKRYmElUXBRT/abS1aurjAss8o3gB8F8tOEjlp9eToNyDdjYdyMN3BoYHUvkASkWZvLT4Z+4G31XjipEvqC1ZknAEgZvHExkXCTj249neIvhMqRpASLFwgyi46OZvG8yHat0pJl7M6PjCJErtyJv8f769/nz3J80qdCEuT3m4l3G2+hYIo9JsTCDWUdmEfwgmM/bfG50FCFyZeOFjfRb1S/5C1DHyXzc7GO5HLaAynLnLEopW6WUv1JqXcrzkkqpv5RSF1J+l8hgnhpKqWPpfiKUUkNNmN/ixMTHMHHvRNp5tuNpj6eNjiNEjiQkJTB622i6Lu6Ku4s7xwYeY1iLYVIoCrDs9OQ1BDiT7vmnwDattRewLeX5I7TW57TW9bXW9YFGQDSwKudxLd8c/zncjrotRxXCat2Ouk3HBR35Zs83vF3/bfze8aOGaw2jYwmDZalYKKXcgW7A7HQv9wTmpzyeDzyfyWI6AJe01teymdGqTD8wnZYVW8pY28Iq7by6kwY/N+BA4AF+7fkrc3rOwdHe0ehYwgJk9chiGjACSEr3WlmtdRBAyu8ymSzjFWDJ495USvkqpQ4rpQ6HhIRkMZbluRt9l0ZujWSAeWFVknQSE3ZPoP1v7XFxcOHAuwd4s/6bRscSFiTTYqGU6g4Ea62P5HQlSqlCQA9g+eOm0VrP0lr7aK19SpcundNVCSGy6V7MPXos6cFn2z/jpdovcWjAIeqWrWt0LGFhsnI1VEugh1KqK1AYcFFKLQTuKKXctNZBSik3IPgJy+gCHNVa38l9ZCGEqRy8eZA+y/twK/IW/+3yXz5s/KEcFYsMZXpkobUepbV211p7ktyUtF1r3Q/4E+ifMll/YM0TFvMqT2iCEkLkrcSkRCbsnkDLuS0B2PP2Hj5q8pEUCvFYuRnX8Fugo1LqAtAx5TlKqfJKqQ2pEymlnFLeX5mboEII07gafpW289vy2fbP6FWrF0cHHqVJhSZGxxIWLls35WmtdwA7Uh6HknyF0z+nuQV0Tfc8GiiVm5BCiNzTWjPv2DyGbBqCUooFLyygb92+cjQhskTu4BaiAAiKDMJ3nS/rzq+jdaXWzH9+vnSbL7JFioUQ+ZjWmqUBS/lww4fEJMTw/bPfM7jpYGxUblqgRUEkxUKIfCrkQQgfbPiAFadX0My9GfN6zpM7sUWOSbEQIh9adWYVA9cN5H7sfSZ0mMCwFsOws5H/7iLnZO8RIh8Jiwlj8KbBLDyxkAblGrD9he3UKVPH6FgiH5BiIUQ+oLVm/YX1DFw3kOAHwYxtM5bPWn0mgxMJk5FiYULhD8OJjI2kWOFiRkcRBciRW0cYsXUE269sx7u0N2tfXUtDt4ZGxxL5jBQLE9pyaQuJOpFnqz5rdBRRAFwOu8zo7aNZGrAUVydXpneezsBGA3GwczA6msiHpFjk0JFbR9h3Y1/ac6UUK06voKRjSRlKVZhVyIMQxu0ax8zDM7GzsWN0q9GMaDkCFwcXo6OJfEyKRRZFx0ez/cp24hPjCYkOYfDGwcQmxv5rOt+GvjKamDCLoMggpvlNY+bhmTyIf8A7Dd5hbNuxlC9a3uhoogAosMXi7N2zfLz5YxKTEmnm3ozm7s1p5t6MEo7/Gh2WU8Gn6LOiD6dDTqe95l3am/WvradIoSJA8glGjcbVyTXPPoMoGC7eu8jkvZOZd3weCUkJ9K7dmy/afEGt0rWMjiYKkAJZLOIT4+m9vDdXwq5QrWQ1xu8eT5JOHteplmstmrs3x8XBhS2Xt1C5eGW2X9mOi4MLK/uspEqJKgDUcK1BYbvCRn4Mkc8dDTrKxL0TWXF6BfY29rxd/22GtRhG1ZJVjY4mCqACWSymH5hOQHAAa15ZQ48aPYiKi+LgzYPsv7Gf/YH7WXNuDRGxEbSu1Br/2/609WzL3J5zKedczujoIp+Ljo9mxekVzD46m93Xd+Pi4MKIFiMY0myI7H/CUEprbXSGf/Hx8dGHDx82y7Kj46Mp/115Wnq0ZP1r6zOcRmtNXGKcXFUi8ox/kD+zj85m0clF3I+9j1dJLwY0HIBvI1+5FFtkiVLqiNbax1zLL3BHFv938P+4H3ufz57+7LHTKKWkUAize5jwkGUBy/jx8I8cvHmQwnaFean2S7zb4F1aV2otXYcLi1KgikVCUgLf7P6GLtW60NKjpdFxRAF1JewKMw/PZK7/XEJjQqnpWpMZnWfQ76l+GV5gIYQlKFDF4v7D+9yPvU/HKh2NjiIKmCSdxKaLm/jx0I9suLABG2XD8zWf54PGH9DOs50cRQiLV6CKxdXwqwAy6IvIUEx8DEsDljL/+Hw8innwZv03aevZNldjPzxMeMhvx39j8r7JXLx3kbJFyjKm9Rh8G/ni7uJuwvRCmFeBKhYy4It4nOO3j/Pi7y9yKewSNUrV4NjtYyw4sYDapWuz+63dlHQsma3lRcRG8NPhn/je73tuR92mcfnGLHlxCb1q9aKQbSEzfQohzKdAFYvU9uCwh2EGJxGWZN6xeby//n1KOpZkU99NdKraiYcJD/n91O+88+c7jN42mpndZ2ZpWXei7jD9wHR+PPRjWpPnol6LpKlJWL0CVSxSb7xL/S0KtjMhZxi1bRRrzq2hfeX2LHlxCWWKlAHA0d6R/vX743/bnxkHZvB2g7dpXKHxI/OHPwznStgVAiMCuR11myNBR5h/fD6xCbG8VPslRrYcSaPyjYz4aEKYXIEqFmfvngWgdunaBicRRrpx/wbjdo1jtv9sitgXYUKHCQxvMTzDPr2+bPsly04t4+0/36ZD5Q5cDb+a9nM/9v4j0xayLUT/ev0Z3mI4XqW88urjCJEnClSxuP8w+T93dtufRf5w4/4Nvtr5FfOOz0Oh+KjxR4xpPYbSRUo/dp5ihYsxvfN0Xl7xMlfCruBZ3BPP4p608miV9rhisYq4ObtR1rmsnI8Q+VaWi4VSyhY4DNzUWndXSpUElgGewFWgj9b6XycDlFLFgdlAHUADb2ut9+c6eQ5ExEYA4FzI2WzrSExKxOcXH9546g0+bv6x2dYjsi74QTDf7P6GmYeTzzv4NvRlRMsRVCpeKUvz9/HuQzevbjjZO8l5B1FgZefyoCHAmXTPPwW2aa29gG0pzzMyHdikta4J1PvHMvJUai+dGy9sNNs6Dt48yLHbx/jPlv+YbR0ia8IfhjNm+xiqTK/Cfw/+l351+3H+o/P80O2HLBeKVEUKFZFCIQq0LBULpZQ70I3kI4RUPYH5KY/nA89nMJ8L0BqYA6C1jtNah+c8bu608mhFI7dG/HDoB7OtY825NWmPLbHfrfwuSSex7fI23lj1BhWmVmD87vF0r96d0x+cZk7POdkuEkKIZFlthpoGjACKpnutrNY6CEBrHaSUKpPBfFWAEOBXpVQ94AgwRGv94J8TKqV8AV8ADw+PLH+A7FBKUa1kNfbe2GuW5QP8ee7PtMch0SFpV9cI8zofep75x+az4MQCbkTcwMXBhb51+/JB4w+oX66+0fGEsHqZHlkopboDwVrrIzlYvh3QEJiptW4APOAxzVVa61laax+ttU/p0o8/4ZgbUXFRrDu/zmxjZF8IvcCZu2fSln857LJZ1iOShT8M5+fDP9N8TnNq/F8Nvt37Ld5lvFny4hJuf3KbWc/NkkIhhIlk5ciiJdBDKdUVKAy4KKUWAneUUm4pRxVuQHAG8wYCgVrrAynPV/D4cxtmt+L0Ch7EP+DN+m+aZfmpRxVDmw1l86XNXA67LONxm1hCUgJbLm1h/vH5rDm7htjEWLxLezPpmUn0faqvDDEqhJlkWiy01qOAUQBKqbbAMK11P6XUZKA/8G3K7zUZzHtbKXVDKVVDa30O6ACc/ud0eWXRyUVUK1mNlhVN3+NsYlIi847Po17ZerSp1AawniOLhKQEFMqixw4/eeck84/PZ9HJRdyOuk0px1L4NvKlf73+NHRrKCefhTCz3Nxn8S3wu1LqHeA60BtAKVUemK217poy3SBgkVKqEHAZeCsX68yVs3fP0qFyB7P8Yfn5yM8EBAewvPdyHO0dcXN2s9hiobXmcthlNl7cyMaLG9l5dSdO9k581uozhjQdYjF/eEMehLD45GLmH5+P/21/7Gzs6F69O/3r9aerV1e5p0GIPJStYqG13gHsSHkcSvKRwj+nuQV0Tff8GGC20ZuyKkkncTvqtlmaKe5G32XM9jG0r9yeF2u9CECVElUsrlicCTnDpH2TWH9+PSHRIQBULVGV/vX6c+zOMT7e/DGexT0JiwnjavhVQmNCSUhKoFKxSlQuUZkKRSvgZO+Eo70jjnaOlC5S2iz3rNyKvMXYHWP59divJCQl0MitETM6z+DVuq/i6uRq8vUJITJXYO7gDnkQQkJSAm7ObiZf9pjtY4iIjWBG5xlp38qrlKjCjqs7TL6unEj94zvHfw6Odo70qtWLphWa0qlqp7RuKRaeWMi+G/t4YdkLACgUxQsXx0bZEBoTmuFybZUtjSs0pr1ne9pXbk+Lii1wtHfMcc6I2Agm753MVL+pxCfGM7DRQN73eR/vMt45XqYQwjQKTLG4FHYJgKolq5p0uf5B/sw6MovBTQc/8kfNo5gHgRGBaK0Na9aJiI1g0t5JTN0/lYSkBAY1GcSY1mMy/HbevXp3vn/2ezyKeVCnTB2qlKiCnU3y7hEVF8XV8KsERQYRkxBDTHwMMQkxXLx3kb+v/s3EvRP5Zs83FLIthE95HyJiI7gZcRM7Gzsc7BwoZFuIQraFcLBN9/gfr9vb2vPXpb8IiQ7hZe+XGd9+vMn/rYQQOVdgisX50PMAVC9V3WTL1FozaOMgXJ1cGdt27CPvJekkbG1sDSkUSTqJX478wpi/x3A3+i6v1HmF8e3HU6VElcfOU7xwcYY2G5rhe86FnKlTpg51ytTJ8P3I2Ej2XN/D9ivb2R+4n8rFK9PaozWJOpG4xDjiEuOITYz93+OEWGITYomMjXzk9YZuDfm63df/6t1VCGG8AlMsLoRewM7GzqSj5C06uYi9N/Yyp8ccihcu/sh7MfExONrlrklm88XNuBV142mPp7M839Xwq/Rb2Y+9N/bSplIbpnSagk95854yKupQlC5eXeji1cWs6xFCGKfAFIvz984/0rSSW5GxkYz4awSNyzfO8L6NhwkPs91+f/3+ddaeW8uf5//k7yt/E58UTy3XWpz+MGtXG685u4Y317xJkk5iXs95vFHvDYu5skkIYd0KTrEIPW/SJqhxu8YRFBXEqpdXZThca0xCDIXtCj9xGVprztw9w8ozK1l1dhVHg44C4O7iTnxSPABtPdtmmiU+MZ5Pt37KVL+pNHRryPLey5/Y5CSEENmVr4vF4pOL2Xp5K52qduJ86Hk6VulokuWeu3uO7/2+5636b9HUvWmG08QkZNwMpbXm0K1DrDqzipVnV6adS2nm3oyJz0ykZ42eTN0/ldn+s/mq7VcMaTbkiVmu37/Oyytexi/Qjw8bf8h3nb7Dwc4h9x9SCCHSybfFYufVnfRf3R+tNb8e+xWAN+q9kevlaq0ZunkojvaOTOgw4bHTxcTHpDVDJSQlsOvaLladWcXqc6sJjAjEVtnSrnI7hjQdQs8aPangUiFt3mN3jtG+cntGtx792OVfv3+d30/9zoQ9E4hPjGfZS8vo490n159PCCEyki+LxfX71+m9vDfVSlZjz1t78L/tT1RcFE+VfSrXy157fi2bLm5iaqeplHUu+9jpImIjOHb7GG+teYu159YSGhNKYbvCdK7WmfHtk7vNftyIfWExYVQuXvlfr9+4f4MVp1fw++nf8Qv0A+Bpj6eZ22OuDOMphDCrfFcs4hLjeH7p88QmxrL65dWUcirFM1WeMcmyHyY85OPNH1PLtRYfNfnoidOmnkhfdWYV3at3p1etXjxb9VmKFCqS6XrCHoZRonCJtOc7ru5gzPYxaV2rNyjXgAkdJtC7dm+5F0EIkSfyXbHYd2Mf/rf9WfDCAmq41jDpsr/b9x2Xwy7z1+t/YW9r/8Rpf+j6AzcibtC6Uuts9WGktSYsJowSjiWIiI1g5F8j+enIT1QuXplx7cbR27u3SU/UCyFEVuS7YpF6RVGnqp1MutwrYVf4Zs839KrVK0tHKjVca+SoWEXFRZGoEzl06xB1fqzDzcibfNL8E75q9xVO9k45iS6EELmW74rFhdALlHQsadIR6pJ0Em+ueRNbZcv3z35vsuVmJDIuEoCtl7dS07Ume9/eK2NiCCEMl++KRWrzTZJOyvD+h5yYcWAGu67tYm6PuXgUM8+Qr6nKOZfj63ZfY29jz5BmQzK9V0MIIfJCvisWbs5uJCQlcDf6rkmOLs7ePcuobaPoXr272UbYS89G2TCm9Rizr0cIIbLDNF+9LYhb0eQuyIMig3K9rISkBPqv7o+TvROzus+SrjOEEAVWvisWqYMbBUXlvlhM2juJgzcPMrPbzLQiJIQQBVG+Kxapgxvl9shi4YmFjN0xlpe9X5Y7o4UQBV7+KxapzVA5PLKIiY/Bd60vr696nWbuzfix24+mjCeEEFYp353g3nxxMwBFCxXN9rwX712k9/LeHLt9jFFPj+Krdl+ZrEtzIYSwZvnqL2FMfAwD1g6gQbkGDPQZmK15V5xewdtr3sbOxo51r66jW/VuZkophBDWJ18Vi00XNxESHcKiXouy3MVGXGIcw7cMZ8bBGTSt0JRlLy2jUvFKZk4qhBDWJV8Vi+Wnl+Pq5Eq7yu2yNP218Gv0WdGHgzcPMrTpUCZ2nJitfpyEEKKgyHKxUErZAoeBm1rr7kqpksAywBO4CvTRWodlMN9VIBJIBBK01mYbENov0I8OlTtk6TzDuvPreGPVGyTqRP7o8we9avUyVywhhLB62bkaaghwJt3zT4FtWmsvYFvK88dpp7Wub85CARAdH00xh2JPnCY+MZ6Rf43kuSXP4Vnck6O+R6VQCCFEJrJULJRS7kA3YHa6l3sC81MezweeN2myHHiY8PCJzUhnQs7Q/rf2TNo3ifcavce+d/bJeBBCCJEFWW2GmgaMANJfj1pWax0EoLUOUko9riMmDWxRSmngZ631rIwmUkr5Ar4AHh4566yvhmsNDgcdfuS1c3fPsfz0cn4/9Tsng09SxL4Ii3ot4rW6r+VoHUIIURBlWiyUUt2BYK31EaVU2xyso6XW+lZKMflLKXVWa73rnxOlFJFZAD4+PjoH66FLtS58tfMr/AL92HZ5G7+f/p0Td04kh6jYkumdp9O7dm/pukMIIbIpK0cWLYEeSqmuQGHARSm1ELijlHJLOapwA4IzmllrfSvld7BSahXQBPhXsTCFLtW68OXOL2k+p3ly8IotmfbsNF6s/SLuLu7mWKUQQhQImRYLrfUoYBRAypHFMK11P6XUZKA/8G3K7zX/nFcpVQSw0VpHpjzuBHxlsvT/0LhCYz5t+SllncvyUu2XpEAIIYSJ5OY+i2+B35VS7wDXgd4ASqnywGytdVegLLAqpWtvO2Cx1npT7iI/no2yYcIzE8y1eCGEKLCyVSy01juAHSmPQ4EOGUxzC+ia8vgyUC+3IYUQQhgr3/U6K4QQwvSkWAghhMiUFAshhBCZkmIhhBAiU1IshBBCZEqKhRBCiExJsRBCCJEppXWOumEyK6VUCHAtF4twBe6aKI45WHI+S84Glp3PkrOBZeez5GxgHfmKaK1Lm2sFFlksckspddjcY2fkhiXns+RsYNn5LDkbWHY+S84Gkg+kGUoIIUQWSLEQQgiRqfxaLDIcYMmCWHI+S84Glp3PkrOBZeez5Gwg+fLnOQshhBCmlV+PLIQQQpiQFAshhBCZsqpioZRappQ6lvJzVSl17B/veyilopRSwx4z/2Sl1Fml1Aml1CqlVPGU1z2VUjHplv2TAdlKKqX+UkpdSPldIt17o5RSF5VS55RSz2Y325PyKaWapHv9uFLqhWzOb7Ztl41sY5VSN9NN2zXde5aw7fJ8v8tGNqP2u45KqSNKqZMpv9tnc/5cbzsT5TPbvmeCbKbd77TWVvkDfAd8/o/X/gCWkzz0a0bzdALsUh5PBCamPPYEAgzONgn4NOXxp+my1QaOAw5AZeASYGuqfIBTum2SOpa6XTbmN9u2y2o2YGxG29VStp0R+102shm13zUAyqc8rgPczOb8Jt12Oc2XV/teDrOZdL+zqiOLVEopBfQBlqR77XngMnDqcfNprbdorRNSnvoBJh+kO6fZgJ7A/JTH84Hn072+VGsdq7W+AlwEmpgqn9Y6Ot02KQw88YqHjD6fqeQ2WwYsYtsZsd9lY9sZtd/56+RRNSH5/0VhpZRDVuc3tdzmy4DJtl9Os5l6v7PKYgG0Au5orS8AKKWKACOBL7OxjLeBjemeV1ZK+SuldiqlWhmQrazWOggg5XeZlNcrADfSTReY8ppJ8qVkbKqUOgWcBN5Lt4NlaX7MtO2yme2jlMPtuemaUixt20Ee7XfZyGbYfpfOi4C/1jo2m/ObatvlNp+5973cbjswwX6XrTG484JSaitQLoO3Rmut16Q8fpVHv2F8CXyvtY5KLsKZrmM0kAAsSnkpCPDQWocqpRoBq5VS3lrriLzOllHcDF7L8FtiDvOhtT4AeCulagHzlVIbtdYPH5Pnn/Obc9tlNdtM4GuSt8vXJB+yv42Fbbs83u+y++/6r7gZvGbSbZcyrzfJTSSdMsmTo/0uD/Llat/Li22X0/3u3+lN2OaXFz8kF7g7gHu613YDV1N+woF7wEePmb8/sB9wesI6dgA+eZkNOAe4pTx2A86lPB4FjEo33Waguam2XQbT/P24z57F+U227bKTLd00nqS0x1rYtsvT/S6r2Yzc70huFjkPtDTB58vRtjNFPnPueybYdibb77K9YY3+AToDO5/w/lgefxK5M3AaKP2P10uTcvIJqALcBErmcbbJPHqicVLKY28ePVF2mRyeKMsoX8oyU0+CVQJuAa7ZmN9s2y6r2Uj5Y5fy+GOS24otZtsZsd9lI5tR+13xlOW/mMP5TbLtcpvP3PteLrOZdL/L9oY1+geYR3L76+PeH0u6P8jAbFKqJsknmW4Ax1J+fkp5/UWSTxQdB44CzxmQrRSwDbiQ8rtkuulGk3w1xTmgiym3HfB6ymc/lvLZn88o3xPmN9u2y2o2YAHJ7fIngD//8R/Y8G1nxH6XjWxG7XdjgAfptskxoExe73e5zWfufS+X2Uy630l3H0IIITJlrVdDCSGEyENSLIQQQmRKioUQQohMSbEQQgiRKSkWQgghMiXFQgghRKakWAghhMjU/we3/iwzksoLLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = tractMAP #wholeMAP    # normally keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "#x2, y2 = origMAP.exterior.xy\n",
    "#plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  6143830 2699135 0.6182777037206635\n",
      "compare to Census 6910840 Leip has Trump + Biden = 2996186.0 0.6182777037206635\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 6910840\n",
    "atlasTrump = 1852475.\n",
    "atlasBiden = 1143711.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 6910835 6910835.0\n",
      "state Trumpers before, after slice opn are 1852460 1852460.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 85 grids for 16 districts\n",
      "starting grid no 0 at x = -74.20216797058823, y = 40.552601100000004 \n",
      "starting grid no 5 at x = -74.0889959117647, y = 40.55260109999999 \n",
      "starting grid no 10 at x = -73.97582385294118, y = 40.552601100000004 \n",
      "starting grid no 15 at x = -73.86265179411765, y = 40.55260109999999 \n",
      "starting grid no 20 at x = -73.74947973529412, y = 40.552601100000004 \n",
      "starting grid no 25 at x = -73.6363076764706, y = 40.55260109999999 \n",
      "starting grid no 30 at x = -73.52313561764706, y = 40.552601100000004 \n",
      "starting grid no 35 at x = -73.40996355882355, y = 40.55260109999999 \n",
      "starting grid no 40 at x = -73.2967915, y = 40.552601100000004 \n",
      "starting grid no 45 at x = -73.18361944117646, y = 40.55260109999999 \n",
      "starting grid no 50 at x = -73.07044738235294, y = 40.552601100000004 \n",
      "starting grid no 55 at x = -72.95727532352942, y = 40.55260109999999 \n",
      "starting grid no 60 at x = -72.84410326470588, y = 40.552601100000004 \n",
      "starting grid no 65 at x = -72.73093120588234, y = 40.55260109999999 \n",
      "starting grid no 70 at x = -72.61775914705882, y = 40.552601100000004 \n",
      "starting grid no 75 at x = -72.50458708823531, y = 40.55260109999999 \n",
      "starting grid no 80 at x = -72.39141502941176, y = 40.552601100000004 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 15150942.002495877 151962634.51961714 59.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 16   #downstate only\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e9fe7b11-da40-4de7-b0b9-42b94aa6e16d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3169 8705\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(isDownstateTract),np.sum(isDownstatePrecinct) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.78263033273482e-09 2.4046084999939926e-06\n",
      "grid 2 is empty\n",
      "grid 3 is empty\n",
      "grid 4 is empty\n",
      "grid 7 is empty\n",
      "grid 8 is empty\n",
      "grid 9 is empty\n",
      "grid 34 is empty\n",
      "grid 39 is empty\n",
      "grid 40 is empty\n",
      "grid 44 is empty\n",
      "grid 45 is empty\n",
      "grid 49 is empty\n",
      "grid 50 is empty\n",
      "grid 54 is empty\n",
      "grid 55 is empty\n",
      "grid 59 is empty\n",
      "grid 60 is empty\n",
      "grid 64 is empty\n",
      "grid 65 is empty\n",
      "grid 66 is empty\n",
      "grid 70 is empty\n",
      "grid 71 is empty\n",
      "grid 75 is empty\n",
      "grid 76 is empty\n",
      "grid 80 is empty\n",
      "grid 81 is empty\n"
     ]
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop\n",
    "isEmptyGrid = [0]*nGrids\n",
    "for nG in range(nGrids):\n",
    "    if gridPop[nG] == 0:\n",
    "        isEmptyGrid[nG] = 1\n",
    "        print(\"grid\",nG,\"is empty\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 5411 tracts\n",
      "I am working on tract number 20 of 5411 tracts\n",
      "I am working on tract number 40 of 5411 tracts\n",
      "I am working on tract number 60 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 68 2.0 3 90.0 0.1274 519648.9\n",
      "I am working on tract number 80 of 5411 tracts\n",
      "I am working on tract number 100 of 5411 tracts\n",
      "I am working on tract number 120 of 5411 tracts\n",
      "I am working on tract number 140 of 5411 tracts\n",
      "I am working on tract number 160 of 5411 tracts\n",
      "I am working on tract number 180 of 5411 tracts\n",
      "I am working on tract number 200 of 5411 tracts\n",
      "I am working on tract number 220 of 5411 tracts\n",
      "I am working on tract number 240 of 5411 tracts\n",
      "I am working on tract number 260 of 5411 tracts\n",
      "I am working on tract number 280 of 5411 tracts\n",
      "I am working on tract number 300 of 5411 tracts\n",
      "I am working on tract number 320 of 5411 tracts\n",
      "I am working on tract number 340 of 5411 tracts\n",
      "I am working on tract number 360 of 5411 tracts\n",
      "I am working on tract number 380 of 5411 tracts\n",
      "I am working on tract number 400 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 417\n",
      "I am working on tract number 420 of 5411 tracts\n",
      "I am working on tract number 440 of 5411 tracts\n",
      "I am working on tract number 460 of 5411 tracts\n",
      "I am working on tract number 480 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 491 6.0 3 90.0 0.1838 206991.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 491 7.0 3 90.0 0.1751 206991.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 492 5.0 0 90.0 0.2714 223918.0\n",
      "I am working on tract number 500 of 5411 tracts\n",
      "I am working on tract number 520 of 5411 tracts\n",
      "I am working on tract number 540 of 5411 tracts\n",
      "I am working on tract number 560 of 5411 tracts\n",
      "I am working on tract number 580 of 5411 tracts\n",
      "I am working on tract number 600 of 5411 tracts\n",
      "I am working on tract number 620 of 5411 tracts\n",
      "I am working on tract number 640 of 5411 tracts\n",
      "I am working on tract number 660 of 5411 tracts\n",
      "I am working on tract number 680 of 5411 tracts\n",
      "I am working on tract number 700 of 5411 tracts\n",
      "I am working on tract number 720 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 735\n",
      "I am working on tract number 740 of 5411 tracts\n",
      "I am working on tract number 760 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 761\n",
      "we have 2 non-opposing shorted wedges for tract no 762\n",
      "I am working on tract number 780 of 5411 tracts\n",
      "I am working on tract number 800 of 5411 tracts\n",
      "I am working on tract number 820 of 5411 tracts\n",
      "I am working on tract number 840 of 5411 tracts\n",
      "I am working on tract number 860 of 5411 tracts\n",
      "I am working on tract number 880 of 5411 tracts\n",
      "I am working on tract number 900 of 5411 tracts\n",
      "I am working on tract number 920 of 5411 tracts\n",
      "I am working on tract number 940 of 5411 tracts\n",
      "I am working on tract number 960 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 963\n",
      "I am working on tract number 980 of 5411 tracts\n",
      "I am working on tract number 1000 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1013\n",
      "I am working on tract number 1020 of 5411 tracts\n",
      "I am working on tract number 1040 of 5411 tracts\n",
      "I am working on tract number 1060 of 5411 tracts\n",
      "I am working on tract number 1080 of 5411 tracts\n",
      "I am working on tract number 1100 of 5411 tracts\n",
      "I am working on tract number 1120 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1132\n",
      "I am working on tract number 1140 of 5411 tracts\n",
      "I am working on tract number 1160 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1170 5.0 3 90.0 0.0729 164759.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1170 6.0 3 90.0 0.0823 164759.1\n",
      "we have 2 non-opposing shorted wedges for tract no 1178\n",
      "I am working on tract number 1180 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1196 6.0 0 118.2 0.0739 92796.2\n",
      "I am working on tract number 1200 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1206 5.0 2 90.0 0.0745 162152.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1206 6.0 2 90.0 0.085 162152.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1206 12.0 0 94.5 0.0694 164149.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1206 13.0 0 94.5 0.0785 164149.9\n",
      "I am working on tract number 1220 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 3.0 0 89.3 0.0527 197414.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 4.0 0 89.3 0.052 197414.0\n",
      "I am working on tract number 1240 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1258\n",
      "we have 2 OPPOSING shorted wedges for tract no 1259\n",
      "I am working on tract number 1260 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1260\n",
      "I am working on tract number 1280 of 5411 tracts\n",
      "I am working on tract number 1300 of 5411 tracts\n",
      "I am working on tract number 1320 of 5411 tracts\n",
      "I am working on tract number 1340 of 5411 tracts\n",
      "I am working on tract number 1360 of 5411 tracts\n",
      "I am working on tract number 1380 of 5411 tracts\n",
      "I am working on tract number 1400 of 5411 tracts\n",
      "I am working on tract number 1420 of 5411 tracts\n",
      "I am working on tract number 1440 of 5411 tracts\n",
      "I am working on tract number 1460 of 5411 tracts\n",
      "I am working on tract number 1480 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1487\n",
      "I am working on tract number 1500 of 5411 tracts\n",
      "I am working on tract number 1520 of 5411 tracts\n",
      "I am working on tract number 1540 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1553 2.0 3 90.0 0.0602 229885.8\n",
      "I am working on tract number 1560 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1569\n",
      "we have 2 non-opposing shorted wedges for tract no 1571\n",
      "I am working on tract number 1580 of 5411 tracts\n",
      "I am working on tract number 1600 of 5411 tracts\n",
      "I am working on tract number 1620 of 5411 tracts\n",
      "I am working on tract number 1640 of 5411 tracts\n",
      "I am working on tract number 1660 of 5411 tracts\n",
      "I am working on tract number 1680 of 5411 tracts\n",
      "I am working on tract number 1700 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1705\n",
      "I am working on tract number 1720 of 5411 tracts\n",
      "I am working on tract number 1740 of 5411 tracts\n",
      "I am working on tract number 1760 of 5411 tracts\n",
      "I am working on tract number 1780 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1798\n",
      "I am working on tract number 1800 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1808\n",
      "we have 2 non-opposing shorted wedges for tract no 1809\n",
      "I am working on tract number 1820 of 5411 tracts\n",
      "I am working on tract number 1840 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1856\n",
      "we have 2 OPPOSING shorted wedges for tract no 1858\n",
      "I am working on tract number 1860 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1871\n",
      "I am working on tract number 1880 of 5411 tracts\n",
      "I am working on tract number 1900 of 5411 tracts\n",
      "I am working on tract number 1920 of 5411 tracts\n",
      "I am working on tract number 1940 of 5411 tracts\n",
      "I am working on tract number 1960 of 5411 tracts\n",
      "I am working on tract number 1980 of 5411 tracts\n",
      "I am working on tract number 2000 of 5411 tracts\n",
      "I am working on tract number 2020 of 5411 tracts\n",
      "I am working on tract number 2040 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2045 4.0 3 90.0 0.1717 202978.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2045 5.0 3 90.0 0.1661 202978.2\n",
      "I am working on tract number 2060 of 5411 tracts\n",
      "I am working on tract number 2080 of 5411 tracts\n",
      "I am working on tract number 2100 of 5411 tracts\n",
      "I am working on tract number 2120 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2131\n",
      "we have 2 non-opposing shorted wedges for tract no 2132\n",
      "we have 2 non-opposing shorted wedges for tract no 2133\n",
      "I am working on tract number 2140 of 5411 tracts\n",
      "I am working on tract number 2160 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2161\n",
      "we have 2 non-opposing shorted wedges for tract no 2162\n",
      "we have 2 non-opposing shorted wedges for tract no 2163\n",
      "we have 2 non-opposing shorted wedges for tract no 2171\n",
      "I am working on tract number 2180 of 5411 tracts\n",
      "I am working on tract number 2200 of 5411 tracts\n",
      "I am working on tract number 2220 of 5411 tracts\n",
      "I am working on tract number 2240 of 5411 tracts\n",
      "I am working on tract number 2260 of 5411 tracts\n",
      "I am working on tract number 2280 of 5411 tracts\n",
      "I am working on tract number 2300 of 5411 tracts\n",
      "I am working on tract number 2320 of 5411 tracts\n",
      "I am working on tract number 2340 of 5411 tracts\n",
      "I am working on tract number 2360 of 5411 tracts\n",
      "I am working on tract number 2380 of 5411 tracts\n",
      "I am working on tract number 2400 of 5411 tracts\n",
      "I am working on tract number 2420 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2433 4.0 2 90.0 0.2656 210785.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2433 5.0 2 90.0 0.2496 210785.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2433 6.0 2 90.0 0.2346 210785.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2433 7.0 2 90.0 0.2205 210785.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2433 8.0 2 90.0 0.2073 210785.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2433 9.0 2 90.0 0.1948 210785.5\n",
      "I am working on tract number 2440 of 5411 tracts\n",
      "I am working on tract number 2460 of 5411 tracts\n",
      "I am working on tract number 2480 of 5411 tracts\n",
      "I am working on tract number 2500 of 5411 tracts\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_119/4287805499.py:67: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
      "/tmp/ipykernel_119/4287805499.py:100: RuntimeWarning: invalid value encountered in multiply\n",
      "  wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 2520 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2524 8.0 0 97.4 0.0717 144265.4\n",
      "I am working on tract number 2540 of 5411 tracts\n",
      "I am working on tract number 2560 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2564 12.0 0 87.9 0.0531 180882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2564 13.0 0 87.9 0.0559 180882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2564 14.0 0 87.9 0.059 180882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2564 15.0 0 87.9 0.0622 180882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2564 16.0 0 87.9 0.0656 180882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2564 17.0 0 87.9 0.0691 180882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2564 18.0 0 87.9 0.0729 180882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2564 19.0 0 87.9 0.0768 180882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2566 5.0 0 125.5 0.0829 82859.1\n",
      "I am working on tract number 2580 of 5411 tracts\n",
      "I am working on tract number 2600 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2615\n",
      "I am working on tract number 2620 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2628 5.0 0 121.6 0.0779 88976.6\n",
      "I am working on tract number 2640 of 5411 tracts\n",
      "I am working on tract number 2660 of 5411 tracts\n",
      "I am working on tract number 2680 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2688 5.0 1 90.0 0.066 169858.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2688 6.0 1 90.0 0.0728 169858.4\n",
      "I am working on tract number 2700 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 2716\n",
      "I am working on tract number 2720 of 5411 tracts\n",
      "I am working on tract number 2740 of 5411 tracts\n",
      "I am working on tract number 2760 of 5411 tracts\n",
      "I am working on tract number 2780 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2785\n",
      "I am working on tract number 2800 of 5411 tracts\n",
      "I am working on tract number 2820 of 5411 tracts\n",
      "I am working on tract number 2840 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 2856\n",
      "I am working on tract number 2860 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2873\n",
      "we have 2 non-opposing shorted wedges for tract no 2874\n",
      "we have 2 non-opposing shorted wedges for tract no 2875\n",
      "we have 2 non-opposing shorted wedges for tract no 2878\n",
      "I am working on tract number 2880 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2886\n",
      "I am working on tract number 2900 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2902\n",
      "we have 2 OPPOSING shorted wedges for tract no 2907\n",
      "I am working on tract number 2920 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2927\n",
      "we have 2 OPPOSING shorted wedges for tract no 2928\n",
      "I am working on tract number 2940 of 5411 tracts\n",
      "I am working on tract number 2960 of 5411 tracts\n",
      "I am working on tract number 2980 of 5411 tracts\n",
      "I am working on tract number 3000 of 5411 tracts\n",
      "I am working on tract number 3020 of 5411 tracts\n",
      "I am working on tract number 3040 of 5411 tracts\n",
      "I am working on tract number 3060 of 5411 tracts\n",
      "I am working on tract number 3080 of 5411 tracts\n",
      "I am working on tract number 3100 of 5411 tracts\n",
      "I am working on tract number 3120 of 5411 tracts\n",
      "I am working on tract number 3140 of 5411 tracts\n",
      "I am working on tract number 3160 of 5411 tracts\n",
      "I am working on tract number 3180 of 5411 tracts\n",
      "I am working on tract number 3200 of 5411 tracts\n",
      "I am working on tract number 3220 of 5411 tracts\n",
      "I am working on tract number 3240 of 5411 tracts\n",
      "I am working on tract number 3260 of 5411 tracts\n",
      "I am working on tract number 3280 of 5411 tracts\n",
      "I am working on tract number 3300 of 5411 tracts\n",
      "I am working on tract number 3320 of 5411 tracts\n",
      "I am working on tract number 3340 of 5411 tracts\n",
      "I am working on tract number 3360 of 5411 tracts\n",
      "I am working on tract number 3380 of 5411 tracts\n",
      "I am working on tract number 3400 of 5411 tracts\n",
      "I am working on tract number 3420 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 6.0 3 90.0 0.0635 185831.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 7.0 3 90.0 0.0656 185831.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 8.0 3 90.0 0.0678 185831.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 9.0 3 90.0 0.0701 185831.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 10.0 3 90.0 0.0725 185831.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 11.0 3 90.0 0.0749 185831.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 12.0 3 90.0 0.0774 185831.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 18.0 0 87.1 0.0565 177785.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 19.0 0 87.1 0.0603 177785.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 20.0 0 87.1 0.0644 177785.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 21.0 0 87.1 0.0687 177785.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3429 22.0 0 87.1 0.0734 177785.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3431 4.0 1 90.0 0.1509 368216.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3431 5.0 1 90.0 0.089 368216.3\n",
      "I am working on tract number 3440 of 5411 tracts\n",
      "I am working on tract number 3460 of 5411 tracts\n",
      "I am working on tract number 3480 of 5411 tracts\n",
      "I am working on tract number 3500 of 5411 tracts\n",
      "I am working on tract number 3520 of 5411 tracts\n",
      "I am working on tract number 3540 of 5411 tracts\n",
      "I am working on tract number 3560 of 5411 tracts\n",
      "I am working on tract number 3580 of 5411 tracts\n",
      "I am working on tract number 3600 of 5411 tracts\n",
      "I am working on tract number 3620 of 5411 tracts\n",
      "I am working on tract number 3640 of 5411 tracts\n",
      "I am working on tract number 3660 of 5411 tracts\n",
      "I am working on tract number 3680 of 5411 tracts\n",
      "I am working on tract number 3700 of 5411 tracts\n",
      "I am working on tract number 3720 of 5411 tracts\n",
      "I am working on tract number 3740 of 5411 tracts\n",
      "I am working on tract number 3760 of 5411 tracts\n",
      "I am working on tract number 3780 of 5411 tracts\n",
      "I am working on tract number 3800 of 5411 tracts\n",
      "I am working on tract number 3820 of 5411 tracts\n",
      "I am working on tract number 3840 of 5411 tracts\n",
      "I am working on tract number 3860 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3874\n",
      "I am working on tract number 3880 of 5411 tracts\n",
      "I am working on tract number 3900 of 5411 tracts\n",
      "I am working on tract number 3920 of 5411 tracts\n",
      "I am working on tract number 3940 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3943\n",
      "I am working on tract number 3960 of 5411 tracts\n",
      "I am working on tract number 3980 of 5411 tracts\n",
      "I am working on tract number 4000 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4011 1 239864.52964299195 0.1749\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4011 1 126706.87658400276 0.0875\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4011 1 318304.7112031194 0.2476\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4011 1 205567.84923341032 0.1676\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4011 1 128561.94885379716 0.1275\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4011 1 141745.42166635938 0.1475\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4011 1 166032.5164623605 0.1575\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4011 1 181225.8732113507 0.1626\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4018 5.0 0 90.0 0.1942 243455.7\n",
      "I am working on tract number 4020 of 5411 tracts\n",
      "I am working on tract number 4040 of 5411 tracts\n",
      "I am working on tract number 4060 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4067 0 339367.0095166038 0.5274\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4067 18.0 0 90.0 0.2637 339367.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4067 0 339367.0095166038 0.2637\n",
      "I am working on tract number 4080 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4099\n",
      "I am working on tract number 4100 of 5411 tracts\n",
      "I am working on tract number 4120 of 5411 tracts\n",
      "I am working on tract number 4140 of 5411 tracts\n",
      "I am working on tract number 4160 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4161 4.0 0 105.8 0.148 187985.7\n",
      "I am working on tract number 4180 of 5411 tracts\n",
      "I am working on tract number 4200 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4210 4.0 1 90.0 0.0916 152568.5\n",
      "I am working on tract number 4220 of 5411 tracts\n",
      "I am working on tract number 4240 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 4250\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4256 3.0 1 90.0 0.0727 123513.5\n",
      "I am working on tract number 4260 of 5411 tracts\n",
      "I am working on tract number 4280 of 5411 tracts\n",
      "I am working on tract number 4300 of 5411 tracts\n",
      "I am working on tract number 4320 of 5411 tracts\n",
      "I am working on tract number 4340 of 5411 tracts\n",
      "I am working on tract number 4360 of 5411 tracts\n",
      "I am working on tract number 4380 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4393 5.0 0 90.0 0.1002 159012.8\n",
      "I am working on tract number 4400 of 5411 tracts\n",
      "I am working on tract number 4420 of 5411 tracts\n",
      "I am working on tract number 4440 of 5411 tracts\n",
      "I am working on tract number 4460 of 5411 tracts\n",
      "I am working on tract number 4480 of 5411 tracts\n",
      "I am working on tract number 4500 of 5411 tracts\n",
      "I am working on tract number 4520 of 5411 tracts\n",
      "I am working on tract number 4540 of 5411 tracts\n",
      "I am working on tract number 4560 of 5411 tracts\n",
      "I am working on tract number 4580 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4586 3.0 2 90.0 0.0734 105594.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4594 5.0 0 90.0 0.1066 161865.1\n",
      "I am working on tract number 4600 of 5411 tracts\n",
      "I am working on tract number 4620 of 5411 tracts\n",
      "I am working on tract number 4640 of 5411 tracts\n",
      "I am working on tract number 4660 of 5411 tracts\n",
      "I am working on tract number 4680 of 5411 tracts\n",
      "I am working on tract number 4700 of 5411 tracts\n",
      "I am working on tract number 4720 of 5411 tracts\n",
      "I am working on tract number 4740 of 5411 tracts\n",
      "I am working on tract number 4760 of 5411 tracts\n",
      "I am working on tract number 4780 of 5411 tracts\n",
      "I am working on tract number 4800 of 5411 tracts\n",
      "I am working on tract number 4820 of 5411 tracts\n",
      "I am working on tract number 4840 of 5411 tracts\n",
      "I am working on tract number 4860 of 5411 tracts\n",
      "I am working on tract number 4880 of 5411 tracts\n",
      "I am working on tract number 4900 of 5411 tracts\n",
      "I am working on tract number 4920 of 5411 tracts\n",
      "I am working on tract number 4940 of 5411 tracts\n",
      "I am working on tract number 4960 of 5411 tracts\n",
      "I am working on tract number 4980 of 5411 tracts\n",
      "I am working on tract number 5000 of 5411 tracts\n",
      "I am working on tract number 5020 of 5411 tracts\n",
      "I am working on tract number 5040 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5052 10.0 0 92.5 0.0723 159287.1\n",
      "I am working on tract number 5060 of 5411 tracts\n",
      "I am working on tract number 5080 of 5411 tracts\n",
      "I am working on tract number 5100 of 5411 tracts\n",
      "I am working on tract number 5120 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 5129\n",
      "I am working on tract number 5140 of 5411 tracts\n",
      "I am working on tract number 5160 of 5411 tracts\n",
      "I am working on tract number 5180 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 5190\n",
      "I am working on tract number 5200 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 5209\n",
      "I am working on tract number 5220 of 5411 tracts\n",
      "I am working on tract number 5240 of 5411 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 5256\n",
      "I am working on tract number 5260 of 5411 tracts\n",
      "I am working on tract number 5280 of 5411 tracts\n",
      "I am working on tract number 5300 of 5411 tracts\n",
      "I am working on tract number 5320 of 5411 tracts\n",
      "I am working on tract number 5340 of 5411 tracts\n",
      "I am working on tract number 5360 of 5411 tracts\n",
      "I am working on tract number 5380 of 5411 tracts\n",
      "I am working on tract number 5400 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5403 4.0 0 90.0 0.2366 211956.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5403 5.0 0 90.0 0.2214 211956.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5403 6.0 0 90.0 0.2072 211956.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5403 7.0 0 90.0 0.1939 211956.5\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0023264496962103\n",
      "Average and max number of wedgePop loops per tract were:  3.5819626686379595 27.0\n",
      "min,average,max of Home District areas were:  0.0 0.017710529295998364 0.2225551764109166\n",
      "calculated statewide vote was 0.29358515960463233, should have been 0.3101675952046603\n",
      "calcd statewide Hispanic pop was 0.24850677954466582, should have been 0.15364391211287076\n",
      "calcd statewide Black pop was 0.1830745228469648, should have been 0.11431784976941081\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.20661615228238772\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "#  and 3/23/22 min of 5pct wedge on map to continue growth\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    if isEmptyGrid[nG] == 0: #NEW 3/18/22 TO SAVE A LITTLE TIME, skip empty grids\n",
    "                        gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                        if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                            for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                                nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                                if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                    usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                    overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                    if overlap > 0 :\n",
    "                                        fracArea = overlap/tractArea[nTT]\n",
    "                                        latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                        loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                            # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])  #leading edge of growing wedgePoly\n",
    "                    fracArea = wedgePoly.intersection(MAP).area / max(0.00000001,wedgePoly.area) #what pct of new wedge piece on map?\n",
    "                    if leadingEdge.intersects(MAP) and fracArea > 0.05 :  #still room to grow in part of edge...\n",
    "                        conclusion = \"keep going\"  #** NEW 3/23/22 - add fracArea criterion on top of leadingEdge criterion *****\n",
    "                    else: #conclude this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "\n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0023264496962103\n",
      "Average and max number of wedgePop loops per tract were:  3.5819626686379595 27.0\n",
      "min,average,max of Home District areas were:  0.0 0.017710529295998364 0.2225551764109166 for  NYC-LI\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start NYC-LI 9:40am - 10:30am\n",
    "STATE = \"NYC-LI\"\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NYC-LI 27Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"27Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"tractVAP\":tractVAP,\"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "24e821dc-ad97-4a68-95aa-c05296c429c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "wholeMAP = tractMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4847 0.7973963351635993 894 pop,GOP 0.7281879194630873\n",
      "6236 0.7918380721351423 284 pop,GOP 0.5985915492957746\n",
      "6277 0.7671765833384918 399 pop,GOP 0.47368421052631576\n",
      "6278 0.7901615597633301 597 pop,GOP 0.6549413735343383\n",
      "6279 0.7734574841952996 441 pop,GOP 0.6167800453514739\n",
      "6280 0.746832134076726 611 pop,GOP 0.7495908346972177\n",
      "6281 0.7660223074849196 376 pop,GOP 0.5824468085106383\n",
      "6609 0.7876610881133798 560 pop,GOP 0.4142857142857143\n",
      "6610 0.7958895741881211 312 pop,GOP 0.3301282051282051\n",
      "6611 0.7869983711990659 532 pop,GOP 0.8195488721804511\n",
      "6612 0.756929876857718 439 pop,GOP 0.6537585421412301\n",
      "6613 0.7420300550786064 672 pop,GOP 0.7291666666666666\n",
      "6614 0.7335514745547339 485 pop,GOP 0.8618556701030928\n",
      "6615 0.7742628065833085 538 pop,GOP 0.7769516728624535\n",
      "6616 0.751746591819583 530 pop,GOP 0.7490566037735849\n",
      "6617 0.7244291013687428 566 pop,GOP 0.7508833922261484\n",
      "6619 0.7377385776171632 318 pop,GOP 0.7610062893081762\n",
      "6620 0.7381346046014012 480 pop,GOP 0.7541666666666667\n",
      "6621 0.7211656927127807 817 pop,GOP 0.8641370869033048\n",
      "6622 0.7279280050606103 713 pop,GOP 0.7727910238429172\n",
      "6644 0.7698693457194336 744 pop,GOP 0.7526881720430108\n",
      "6645 0.7659307270870962 513 pop,GOP 0.6939571150097466\n",
      "6646 0.7991420442947915 545 pop,GOP 0.763302752293578\n",
      "6648 0.7986511879141498 475 pop,GOP 0.7957894736842105\n",
      "6669 0.7589859589251114 531 pop,GOP 0.7099811676082862\n",
      "6670 0.7742190967079817 468 pop,GOP 0.6239316239316239\n",
      "6671 0.7523053917797256 473 pop,GOP 0.7864693446088795\n",
      "6698 0.7836533681641298 425 pop,GOP 0.5576470588235294\n",
      "7816 0.771423387030625 16 pop,GOP 0.25\n",
      "7967 0.758284710608786 713 pop,GOP 0.2061711079943899\n",
      "7968 0.6616165497018326 713 pop,GOP 0.29873772791023845\n",
      "7969 0.7705104701188004 575 pop,GOP 0.26260869565217393\n",
      "7970 0.7651723950989118 663 pop,GOP 0.4479638009049774\n",
      "7971 0.7835219819170192 760 pop,GOP 0.3973684210526316\n",
      "7972 0.6479455464616332 793 pop,GOP 0.27490542244640603\n",
      "7973 0.6382720299503389 704 pop,GOP 0.2571022727272727\n",
      "7977 0.7762947548079833 492 pop,GOP 0.6056910569105691\n",
      "8004 0.7343765146137333 246 pop,GOP 0.4634146341463415\n",
      "8008 0.6673418513884287 712 pop,GOP 0.2893258426966292\n",
      "8021 0.49683618110468475 806 pop,GOP 0.8225806451612904\n",
      "8022 0.5047160736261539 831 pop,GOP 0.7677496991576414\n",
      "8023 0.5135933532915636 774 pop,GOP 0.7855297157622739\n",
      "8024 0.521258398200558 885 pop,GOP 0.7819209039548023\n",
      "8025 0.5199817260614016 904 pop,GOP 0.831858407079646\n",
      "8026 0.5085847359190832 738 pop,GOP 0.7195121951219512\n",
      "8027 0.520847199163893 783 pop,GOP 0.7701149425287356\n",
      "8028 0.5372831957543378 805 pop,GOP 0.7527950310559006\n",
      "8029 0.5163193744022629 382 pop,GOP 0.7643979057591623\n",
      "8030 0.5258102374351795 552 pop,GOP 0.7572463768115942\n",
      "8031 0.5628036052397412 800 pop,GOP 0.77375\n",
      "8032 0.5386106899984924 708 pop,GOP 0.7641242937853108\n",
      "8033 0.5647102798917422 887 pop,GOP 0.7508455467869222\n",
      "8034 0.5329520429185866 605 pop,GOP 0.7421487603305785\n",
      "8035 0.5345317184041193 784 pop,GOP 0.7219387755102041\n",
      "8036 0.5448115601999062 793 pop,GOP 0.6569987389659521\n",
      "8037 0.5573222940042096 570 pop,GOP 0.7842105263157895\n",
      "8038 0.5587259294972978 616 pop,GOP 0.8295454545454546\n",
      "8039 0.5863907552384576 687 pop,GOP 0.7510917030567685\n",
      "8040 0.5998661169937649 786 pop,GOP 0.7837150127226463\n",
      "8041 0.616097572407801 777 pop,GOP 0.797940797940798\n",
      "8042 0.6069502214882002 773 pop,GOP 0.7671410090556274\n",
      "8043 0.6231729286022997 671 pop,GOP 0.797317436661699\n",
      "8044 0.6337734279076546 732 pop,GOP 0.6830601092896175\n",
      "8045 0.640225338677671 698 pop,GOP 0.7994269340974212\n",
      "8046 0.665499283636339 767 pop,GOP 0.5801825293350718\n",
      "8047 0.6751888330909023 900 pop,GOP 0.7377777777777778\n",
      "8048 0.6896270496476609 807 pop,GOP 0.734820322180917\n",
      "8049 0.5471491717794044 795 pop,GOP 0.7710691823899372\n",
      "8050 0.5486159718996862 880 pop,GOP 0.7625\n",
      "8051 0.5725142205256215 826 pop,GOP 0.7457627118644068\n",
      "8052 0.5938879834771428 737 pop,GOP 0.7788331071913162\n",
      "8053 0.6002175646943817 796 pop,GOP 0.7311557788944724\n",
      "8054 0.6266567678628155 910 pop,GOP 0.7527472527472527\n",
      "8055 0.6512690214393898 787 pop,GOP 0.772554002541296\n",
      "8056 0.694395702362095 719 pop,GOP 0.7649513212795549\n",
      "8057 0.6636908215781129 823 pop,GOP 0.7861482381530984\n",
      "8058 0.7172975054070293 819 pop,GOP 0.7277167277167277\n",
      "8059 0.7163627430889768 819 pop,GOP 0.7692307692307693\n",
      "8060 0.6885482740978609 852 pop,GOP 0.7394366197183099\n",
      "8061 0.6572648768704225 805 pop,GOP 0.7627329192546584\n",
      "8062 0.6732932858611471 864 pop,GOP 0.7326388888888888\n",
      "8063 0.7007193542353111 827 pop,GOP 0.7158403869407497\n",
      "8064 0.7071210309443148 753 pop,GOP 0.7144754316069057\n",
      "8065 0.7108781584887041 690 pop,GOP 0.8\n",
      "8066 0.7312031458560978 797 pop,GOP 0.7440401505646174\n",
      "8067 0.7176003231149305 747 pop,GOP 0.7670682730923695\n",
      "8068 0.732688037967604 733 pop,GOP 0.7203274215552524\n",
      "8069 0.743227997068259 746 pop,GOP 0.6769436997319035\n",
      "8070 0.7819239841644011 845 pop,GOP 0.7727810650887574\n",
      "8071 0.7659510350853265 781 pop,GOP 0.678617157490397\n",
      "8073 0.7982707633570318 792 pop,GOP 0.7335858585858586\n",
      "8074 0.7936996445588017 819 pop,GOP 0.6874236874236874\n",
      "8082 0.7144528695665955 806 pop,GOP 0.7332506203473945\n",
      "8094 0.6672187233581295 786 pop,GOP 0.811704834605598\n",
      "8095 0.6690529659124748 815 pop,GOP 0.7337423312883435\n",
      "8096 0.5284437037694479 855 pop,GOP 0.8175438596491228\n",
      "8097 0.5047543714143707 854 pop,GOP 0.8079625292740047\n",
      "8098 0.5399602527670239 850 pop,GOP 0.7941176470588235\n",
      "8099 0.5508018350165353 553 pop,GOP 0.7902350813743219\n",
      "8100 0.5359758253306917 938 pop,GOP 0.7196162046908315\n",
      "8101 0.5591379692487609 897 pop,GOP 0.7714604236343366\n",
      "8102 0.5871702086389496 738 pop,GOP 0.7859078590785907\n",
      "8103 0.5922157575274376 877 pop,GOP 0.8004561003420753\n",
      "8104 0.62531457177755 848 pop,GOP 0.8077830188679245\n",
      "8106 0.7155505046244309 725 pop,GOP 0.7420689655172413\n",
      "8108 0.6184228411214748 831 pop,GOP 0.6630565583634176\n",
      "8109 0.5886227973156775 787 pop,GOP 0.567979669631512\n",
      "8110 0.6213740778810602 516 pop,GOP 0.5445736434108527\n",
      "8111 0.6967603693520752 808 pop,GOP 0.719059405940594\n",
      "8112 0.7041309428588775 689 pop,GOP 0.737300435413643\n",
      "8113 0.7000002466285878 693 pop,GOP 0.6782106782106783\n",
      "8114 0.6447901769958931 663 pop,GOP 0.555052790346908\n",
      "8115 0.6352100045759972 825 pop,GOP 0.6254545454545455\n",
      "8118 0.7334006809909848 645 pop,GOP 0.5534883720930233\n",
      "8119 0.7762616158090797 596 pop,GOP 0.5184563758389261\n",
      "8120 0.7394864031254156 746 pop,GOP 0.45576407506702415\n",
      "8121 0.7626777473918083 701 pop,GOP 0.5820256776034237\n",
      "8122 0.7799538896581788 805 pop,GOP 0.5788819875776398\n",
      "8123 0.747806520035743 538 pop,GOP 0.5594795539033457\n",
      "8125 0.6213155502234996 817 pop,GOP 0.6560587515299877\n",
      "8126 0.5456190398783515 580 pop,GOP 0.6206896551724138\n",
      "8134 0.6947019456801209 797 pop,GOP 0.6072772898368883\n",
      "8135 0.7185652445726678 598 pop,GOP 0.6488294314381271\n",
      "8136 0.6514761772390271 728 pop,GOP 0.6126373626373627\n",
      "8137 0.7119445453035428 759 pop,GOP 0.5085638998682477\n",
      "8138 0.7438522817469616 759 pop,GOP 0.5085638998682477\n",
      "8139 0.6551404067332657 807 pop,GOP 0.5576208178438662\n",
      "8140 0.64384225025093 909 pop,GOP 0.6017601760176018\n",
      "8141 0.6490815419710516 798 pop,GOP 0.6804511278195489\n",
      "8144 0.695162624025291 767 pop,GOP 0.3963494132985658\n",
      "8145 0.5711608478813905 736 pop,GOP 0.27445652173913043\n",
      "8146 0.6042400108678515 671 pop,GOP 0.22652757078986588\n",
      "8147 0.5403790364464525 415 pop,GOP 0.27710843373493976\n",
      "8148 0.573144988779223 385 pop,GOP 0.08571428571428572\n",
      "8149 0.5919492228168398 515 pop,GOP 0.07766990291262135\n",
      "8150 0.587416569005476 671 pop,GOP 0.14157973174366617\n",
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      "8152 0.5522209429555841 588 pop,GOP 0.13945578231292516\n",
      "8184 0.7176989929485822 691 pop,GOP 0.5701881331403763\n",
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      "8186 0.7142049980061378 627 pop,GOP 0.6347687400318979\n",
      "8187 0.6506851164103217 710 pop,GOP 0.33380281690140845\n",
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      "10501 0.7440998906221274 149 pop,GOP 0.6174496644295302\n",
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      "14729 0.7743935910041473 342 pop,GOP 0.14619883040935672\n",
      "14741 0.5810341010954039 778 pop,GOP 0.46272493573264784\n",
      "14742 0.5559065337862251 393 pop,GOP 0.5089058524173028\n",
      "14758 0.7879451569141351 515 pop,GOP 0.13592233009708737\n",
      "14762 0.7759019921324996 651 pop,GOP 0.23809523809523808\n",
      "14777 0.7842794667379273 368 pop,GOP 0.17391304347826086\n",
      "14782 0.7761547077307862 181 pop,GOP 0.35359116022099446\n",
      "14783 0.7210854097305609 735 pop,GOP 0.5619047619047619\n",
      "14784 0.6801993827881314 804 pop,GOP 0.5659203980099502\n",
      "14785 0.6439211499367699 954 pop,GOP 0.23060796645702306\n",
      "14786 0.5944503821768351 706 pop,GOP 0.26062322946175637\n",
      "14787 0.7238309128752222 405 pop,GOP 0.4\n",
      "14788 0.6311225000594733 719 pop,GOP 0.5618915159944368\n",
      "14789 0.31646749774770006 556 pop,GOP 0.5251798561151079\n",
      "14790 0.6464177675957141 700 pop,GOP 0.3557142857142857\n",
      "14791 0.6111456651654698 820 pop,GOP 0.2658536585365854\n",
      "14795 0.000514592909993036 443 pop,GOP 0.43340857787810383\n",
      "14796 0.7357931922933759 404 pop,GOP 0.26485148514851486\n",
      "14797 0.7098097691346035 923 pop,GOP 0.1722643553629469\n",
      "14986 0.7990785079347537 756 pop,GOP 0.1746031746031746\n",
      "14996 0.7996020273355047 772 pop,GOP 0.17098445595854922\n",
      "15018 0.7955775150295947 433 pop,GOP 0.16859122401847576\n",
      "15019 0.7758850312668762 331 pop,GOP 0.22054380664652568\n",
      "15020 0.7877639280494739 571 pop,GOP 0.14010507880910683\n",
      "15035 0.7583568408753505 1067 pop,GOP 0.2858481724461106\n",
      "15036 0.7987427472865761 692 pop,GOP 0.2861271676300578\n",
      "15067 0.7738947263334199 393 pop,GOP 0.4122137404580153\n",
      "15068 0.722964126763046 1013 pop,GOP 0.25765054294175715\n",
      "15191 0.756707105609275 664 pop,GOP 0.2469879518072289\n",
      "15192 0.7283496293091045 670 pop,GOP 0.21940298507462686\n"
     ]
    },
    {
     "data": {
      "image/png": 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NYmIi/ac9zphJb1CZG4KLh8Bd50ZW0Tly0gvYtGkTERER9B8wDIfDyCuvPENMTAwLFy4kMTGRAQMGcPToUYAaj0GgZoP82LFjSJJEXFxcbU0F4LQsbksslmJKSnZgNGaQX3CSkpI9pJ/P4cw5A999H85PP3Vi/rwlrDhwHKvdDgJMejPDor356mQxgup/+JuoL4Bqcz49PR2j0cjAgQPp1q1bjTnt7e1d41++b98+Xn31Vby8vG6yhE6cQFVVMunp88jL/wkPjxbUj5vF21mCL7IziXXV8F5sMBOTczh7NJWQmBCSK9KZK8VgNNqxCgP3Pt2XvAnrSM17mzbqxnTo0IGcnBxcNI0A2Lx5C/7+MTz22GMMGzaMuLi4mhowTZs2ZdeuXbRv3561a9cya9Ys9u7dy5kzZ7jrrrs4d+4cWq2W2NhYWrdufVPnxaksbjHsdhN6fTLu7k0u+31e+kFOJI8CefmF9jrAjsWiwe6QUXjcizMH9iCEYGTHeA7murNq/wq84nREpigACRC4KjSU20w3bVxQnUdq06ZNaDQa1q5dy8aNG5k2bRqBgYH07duXiRMnkpWVxYABA9BqtXTo0IHZs2cTH3/DlmGdOLmEqqoz7E8aADiICH8Cl+AnGHAii6OVRsbU8eP16GAkSeK+AB/u3fElOzV16HFMjxIZOoWWSgrZufwsrmo3gkKCWL3uZyIjI3nkkUd44YU30OoUnD59nqlTZ/Hjjz/y6aefUqdOHdasqc69+vbbbzNy5EgsFgt33303cXFxxMXF8cgjjwDw+uuvEx0dfdMVBTiVxS1HTs73JJ+dRr3YaWzcaK1ZD50zZw4q6wFySj5AkjnI3h3BupM+7N69Fw8PD6KimmP3KGT0S0ewORxIwMeJh7BccHISh8qYYQHHhS2LCtvl3WFvNAqFgpUrV9akA5HL5ajVar755hvuuecefHx8kMvlHDhwgIyMDMaMGcP27dtrRVYn/7+w2fQcO/40CoUOj9h5rE/9jg9zUrAJmNcwnH5+njWxRS4KOY8168yOwysBeDGgkDeNVag9g0g5VEhMqwDiy5szcuRIevTowblz5xg3bhwzXxrMt9+8Te/ed9O/f38WLFhASUkJgYGBAERFRbFly5a/lPH111+/4fPwVzj3LG4hhBBsPv8LAEkHXubDD99hxIgRVJQV0Klja3YenobDouHnRRFMfH8va379DaPRSHp6Ojt37sRSbqHTG02J8PVGAOUGA546FQq5ApkMTH/YzxaXi6e4wTRo0AC73c6bb77J0qVLEUIwYMAAXnrpJdLS0khMTGTv3r107NgRnU6HSqWqiQp34uRGIoTgzJlXMRjSiI2dSuHxgcQalnO/9WPWtYzhHn+vPwWhduvYjeCzB+mvPkNvbRwqjSvachUAZ/fn06VLl5rAu6SkJKKjo/HybE3Tpmq2b//hkvO3A07X2VuIcnM5X3zZmqaxNvbtM7JzRxXp6f688bKKZ148h7vOl42b9tCxTUd0kmBo38eYvepz7r77br76chEWmwW5DOxChnA4iPCvj4ulnCxDBUabHtsfK6gr1GCzcNkgvBuEi4sLERERPPbYY5SVlbF06VJ0Oh3vvfcegwcP5oUXXuDo0aMMGzaMO+64g969e/P888/To0ePmyajk/+fZOd8z+nTk4mMGEdZeRKlpbsu+X7niUf57ds1SJLE3LlzadGiBQALFy5k2rRplJaWotUEYKgqwdc9mEkPfUzH0f4kJCRgNBrx9/dn+/btCFFB9+5NyMmRA0oqKiooLCysyXxwLdxo11mnZXELYRd2KpPlSBYwZMswmhzExBSi8zXj4xmCAy/27T9Ck3qxGC0WEjomoDOrSDDFYbFacJGDVq1EXHC/yyvNRJIEsUEBeNeLR671Army+maShCST+J+Xpb/lj/mgrpQ/vo1ZLBbOnDnD7Nmz2bBhAxqNhuTkZHr37k1sbCze3t7s3r2bnj17MmbMGHr37u1UFE5uOJVVp0lOnoq3VweEsFNauou4+jPo0Pkk5zU9qay089Wct3jnm3cuySYLMPDBh/hp1x5emj2Xx554kBCfKO5t+xghsV689tprLF++nPLycsLDwykrKyMoKJYvFnZk67aHWL9+PXfcccd1URQ3A6eyuIWw2q0c95ahOSRD62PHpPdH5yZj4+ogzpwrIDMzk507d6J10aDVqNlwIBGDzcTobychAEmtpWGLtgBIkgybMHOqLB/h44k8PAa7vhTsVlCqwWpCOKrzQink8r+U6eLD3svLC4vFUnO+R4+wKxrTHy1XnU5HWFgYw4cPZ+fOnRw/fhyDwUB6ejpHjx6le/fupKWlMWnSJIKCgnj66aevYhadOLlybLYqjh9/CoXCg6CgwZxP/4SgwAEEBd2HRqFmZPvPKLO+Sr0mHlSkPk6uPLUmmyzA2x98wE/zP8N8PpW9e/eSnH+UbVlL8Wtp5vDhw3Tq1AmA3r17M3nyZLp3784z486wZfNWlixZQlVVVU19Cy8vL3755ZfanI6/xaksbiFKTCUUu4HmiIz6zdWkpxSTdUbJz78V0bRpU0JCQli1ahWVVVXEhgRx6PxxisxlGG1m5HI5RpOZ1GITSDKQ4PvvvycgIIDk5GTyNi4HmQwUSiStFrlcws1VjQTY/ibL68WH/fPPP1/zBiRJEtu2ZSBJcLkthcvtM+h0OoYPH05JSQn3338/VquVDz74gK5duzJkyBB8fX2pU6cOSUlJzJo1i+3bt9OlSxcGDRp0XebWiZP/RQjB6dMvYzCkExv7KslnX0erjaZevamXWMQqi4Z6sQ9QJgVSce5JJLWdkpISVhw6isZa/QJlNBrZvWcPz08Yz/fLvuWZZ565JMAuIyODkpISNm3axMqVM2jQ0Mqvv67it99+IzExkfXr1+Pp6UnPnj1v+jxcKc7dw1uIImMRJW6gyJNwc5NzV2cPFv+Ug4+fnHffe58pU6aQm5vLsapKnr4rgVl7dqHXV9fZVgiBRYK8U0mAQKNxZdOmTQgh8PLywpyfj9VmA+FAYShDqYZG7QPJPVTA+WLjBYfay+Ph4cHx48fRarWUVlQieXjiIRno0lnip5/K0elkBAf7YzQqyc3NxdvbG71ej0wmw9/fn8zMTKxWK4sWLWLYsGE0bNiQ7OxsVq1ahVwux2q18s4776BWq4mPj8dqvdkRIE7+P3KxqmRk5HNkZSzFbjfSuNHHyOWul0RmP/DAAwijg4RW37I5aTiZKRu5485ITEYviopM9OrbF8nhwGI2s2njRjZt3Mj58+dr4oR69OjB3r17kSSJRo0aERPjQqPGelq0aIhOV11/5tdff6V79+5XtdR7s3Aqi1uIMnMZdrlEydBqf9eWwdFkxHlyqqyUt99+m88//5wxY8YgGar4aM1mssuLUatlqM0OKhwO1C6uaDQuVJaXYjab+PTTT9FqtXTq1IlmzZrxyiuvgACZXElcA4mzR3KQPKLx1p+ixFT9xyAHzECciwsNo2JYceo4DRs25JtvvuHVV1/lR5Mg5P5hfBtdzsGDQ6gfp2blCgdyhZ7Y2AROnjyJTqdj0qRJbNmyBSEE9913HzNmzLhkrCEhIWzbtu2mz7ETJwCVlSc4e/YNfLw7ozgRRJnrHsL8xuDiEkn68d3MmTOHPXv2kJ2dzQMPPIDNZuM9tRdxge8SGNSaWbOqCxM9PkZHt4QEvv/+ezw9Pdm3bx9QnaojODiYXbt2sWbNGoKDg7nzzjvZtm0ddULh66U2Zs8eWyPP0qVLb/llV6eyuIVoHdiadlVK7HEGHBY1xsIgovzlxHXtwuw5c4Dq1BnP9OmJm7c3U4N/JGFmCTvOFJNisWC2Wig3GlCr1bi7u9OvXz/y8/PZuXMna9euRSaTIZPJMBlMDB8+mMOHVuAoOo1crkClgj6xYaxLyQWHg7NWK6dOHAWFgrEfzOZwhYFXXn+djBPn2VBcgaRtTpPGn9EloSNPP7WDo8cep0njcTVvSm+//XYtzqQTJ3+NzVZZHU8h98R3813YyjSQIGE6W87ZgndZuX82zWKbolKpiIyMxGQy8cQTT5CQkEBJZS4TXxrLuXPLWLdORVlZGU2aNGHWrFnUrVsXAKvVisPhYMaMGbz00ktYLBaCg4Pp3fsOUtN+pWOHLnyxYAN3330vAGVlZRw9epSEhIRanJV/xrlncQsRoA1gYu85SMUqJIUFr+hgQtxlrF+7HqvVSkZGBjqdDktlBSYPLQCFWsE5i4WHvLwZNmQIKpWKVq1a4ebmxqxZs/jll18YOXIkfn5++Pj44O3tzV133cWyZasIDAxEkiTUajXduvXgrR/WoNJomDBhAv7+/qg0GrQDH2SCUcFdB5JpsvME5TY7dgE2AX5+PZHLXfDx6YpK5U92Tu0lOXPi5EoQQnDq9GRMpiz8V4dDQTAKizsupbEU6n4i27GIykoHyE7juLCX5+HhQb9+/Vi2agkfz5YREbSK6Gg1Pj4WGjRowOrVq+nVqxcVFRWMHTuWsLAwXFxcaNWqFVu2bGHbtm0YjUZGjRpNu3Yq9Pr2jBo1qmZvb9myZQwcOPCSvFC3Ik7L4hYjJqIjASxn9+EnCe28Coc9mI7WJiQkJCBJEh9+OJPvp7zA+WIr2l4ONuqrMArBivIyuprNKBQKKioqeOONN+jWrRsajQaFQlGTwfLo0aPExMTg4+NDRUUFjz32GP7+/iQmJrJhwwbMZjOffPIJjRo1QqlU4nrqMK/6adD6B/L++Tx2l+kJVivxVf3+pyOTKfDz60Fu7opanDknTv4eIQRpabMpKFhD3YjnsSWtxFbnCIrAprgVtMTofQaAKPe+nDEvJ+vA94S1fpC0tDT69+9PhaGIcWPN+If74qGuZMf2IqKjVSxatAiothAKCwuJjIxEqVTy8ccf8+GHH9KtWzcOH95L377RfPxRKR06rOObb76pkWvp0qV8/PHHtTEl/4orVmWSJMklSTokSdKvF44HSZJ0QpIkhyRJfxkIIknSXZIknZEk6ZwkSS9dD6H/67hHNKLzHatwGGMJ755Jh/hsdu3axc6dO2kYG0uwpxtd+w5lW3oWb8Q66FY/FINCQWZmJpGRkdjtdrKzs9m3bx/btm2jY8eO+Pv74+PjQ0xMDAD16tXDYrHw0EMPsXHjRuRyOZmZmTgcDux2O0ajEYvFgoerK82VEn39PfmiUQQALdxd/ySzyZSNq2vkzZwmJ06uGIfDyqnTk0g7P5fAwP64/mrFmpWFz7A45Lp01q01MH6MnskvaImKH8KJow7OZs3l8OED5OfnYxc2bJZcnh6Xw/rVbcnKtFBWZmfHjnTy8vKwWq2EhoaycuVK6tevz65du5g0aRJlZWV89913PPvsYCa+4EGdOsE4HA4GDhxYUwlv27Zt9OvXr8aF9s0336zl2foLhBBX9AOMB74Bfr1wHAfUAxKB+L+4Rg6kAFGACjgCNPine7Vs2VI4EcJkqBQrFjYXGzdFidVLhgqHwyEKzqeK9+/vLQZ/slqI19zF2odcxTPD7xVffPGFaNeunXB1dRU7d+4Uhw4dEu+++25NX8HBweKVV16pOT5+/Lho2rSpcHd3F/7+/qJZs2biwIEDokOHDuKll14Sbdq0ET4+PmL48OGXyJRlNItSi/WScw6HQ2zb3lqcOPH8jZ0QJ06uAqu1Qhw8OExs3BQlUlI+FOUb1ouT9eNE9gsvCiGEOP/jWtEoIEbkfLVVpKamilZNm4tpg+8TDRqoRWS4v+jdr5d4f2598fDIQKHVuoqtW7eKdv36CRcXSTw/sakYN26ciIyMFGq1WiQkJIj4+HgxePBgUadOHbFkyRKhUqlETKyraNLET7Rt21Zs27ZNCCFE9+7dxalTp4QQQtStW/eaxwkkiSt8nl/NzxVZFpIk1QF6Awv+oGROCSHO/MOlrYFzQohUIYQF+A6458rUmBO1i4741t9Res4TdfAuEn8disVUnSl2r1f1RnKbEDmr1m9l/vz5WCwWYmJiaN++ujLXxIkTSU1NJT4+nsrKSnbs2EFWVhYADRs25PDhwxQWFhIfH8/69etp2rQpBoOByZMns2PHDsLCwnj33XcvkSlEo8JTeenqpdmSj8VShJt745swK06cXDl6/TmSDtxPadle4uLeIbC4Aznjn0fTuDGBr78GwNGyPFoF1oNsI6HBIVTklDIgeBjvTavL/YMlNLoTxMbKeXjMYhwOQUxMDG7R3bHK3Nmx3YVVq1aRl5dHq1atWLZsGfv37yc8PJzw8HB0Oh1qtZxPPw1k67a1lJaWXhKod9EjUAhB165dueuuuzh8+HBtTdffcqXLULOAFwDHP7T7X0KAzD8cZ1049yckSXpMkqQkSZKSCgsL/+Vt/ruENYyl/8g9iLJmOLR7OHDgfkITcpD7Q3SHNWS1GINDqa0JIhJCcPjwYd577z0APvnkE0JCQnjzzTd5+OGHayrRzZw5k65du3LnnXfy5JNP4ufnh1wu55133uHuu++mQ4cOjB49moCAgH+U0WTKBsDF5cqiup04udHY7SZSUj5g774+mM35NGu6EM/cKLKeehplnTqEzvsMmYsL5atXc3ryZNzsRuzlrmTM2omnxoOqRhKOsnZ4eEkIUyk2t1c4tvsU7u7ueHl5cSY9B7uxkn79+vHwww9jNptxcXEhOzsbk8nExo0biY2NZePGdQQGSnh7dcDTo+WfKuEVFxcD1ZUut2zZwjvvvMNDDz1UW9P2t/yjspAkqQ9QIIQ4cBX9Xy7z0GVjv4QQ84UQ8UKIeD8/v6u41X8XuVxJt3uXozLdhTbQgE/9ciJJpUqh5ce0ZdzbfwC7d+8mKSmppqLWxIkTgWoLolu3bjz99NOUlJTg7+8PwPjx49myZQtbtmyhV69eNffq2bMnO3bsYO/evTzxxBNXJJ9C7gaA3aa/ziN34uTfYbWWkpX9DXv33c359E8ICOhN25ZrsH99kPQHHwKNmtB5n6G4EDBnPnUKD7mcsuJUAFSlCsrMFTR5oAcd+05HlRbN3m0SLet0ZuHChYSEhFBocGCUuaJUqXnuuedwd3cHqt3a8/LySEtLo6qqivXr1/PllwspLjazfXv1i9TlKuEB+Pr6AtXFj7Ra7S1ZJfJKLIsOQD9Jks5TvYzUTZKkpVfYfxYQ+ofjOkDOv5LQCVD9R9ap18c0afwpAEq9oItlM9s2Wlm5ciUdOnTg4MGDeHh4UFJSAlRbD/PmzWPy5MmoVCqmT5/OqFGj2L17N+3bt6dz5841FgjAvHnzaNOmDZ06dWLTpk1XLJtaHQjIqKw6dV3H7MTJPyGEA4MhjdzcHzly9DG272jHmTNTkMtcaNpoIUGHWpB13wiKPv6k+gKLFVXo748kvwkTuPe33zjmpqQy/yjZFfkoZAKLsYovnh4FZR50iAinXZM4Tp06zceffErqmROYc5ORK5QkJSWRlpaGUqnE19eXM2fOEBcXx7x582jbtjV+fjI6dY5g3LjpwO+V8ADWrl1L586dMZvNmC4sL2dnZ1NWVoanp+dNnccr4R9dZ4UQk4BJAJIkdQGeF0IMvcL+9wMxkiRFAtnAEODBq5LUCQAWS7Ui6Hj8JOVlMrYcs3P//QMZP348w4YNo6qqquZtZfz48SQlJfHCCy+wYMEC2rZty+TJk9m3bx8rVqwgLCyM3r17c8899+Dp6cm8efPYu3cvJpOJrl27snfvXuR/k2TwInK5BrncFav11nsbcnJ743BYMJsLMJvzMJvzMF34NJvzMZtyqdKfxW6vAkCtCiC0znACA+/BVRFFxkNDMZ08iToujjoff0TJ4sUY9u6jctt23DpX7xtIkoR/w4Y89eKLPDB9GnlF5chkdpq3aE6ISsm9zRvSOiqUEoOB4wYVXQaNxFWloKool+DQaPr06YPBYECj0bBt2zYSExMpKChg3bp1HDp0CKUSAgI1vPnmm7z88suXrYSXmZnJPffcg1arxW63M2/evD/VzrgVuOo4C0mS+gNzAT9gtSRJh4UQd0qSFAwsEEL0EkLYJEl6ClhHtWfUQiHEiesi+f9TLj6QbVY12dnn6datG7t376ZOnTqUlJTg6emJWq2uaS+EQKFQkJaWxoQJE1iwYAHl5eWEhVWbxfHx8SQmJtKsWTMaNGiAUqlEqVSi1WpJSUkhNjb2H2VyOCw4HGZkkvLGDNrJ/zvKyw+SljaX4pLt/O/KtVzuilodiFodQGBgP9zdGuPm1gidrh6SVP1yYyspwXT2LMrQUKJ+rK5mpwwLI61vPz59Zhw/ubhcUptixIgRGH75kNfW5ODh7obVbGRPZi4ySaJfswZER0RyMjkHL5UCjZc/fp46Pvngbc6dPsErr7xCcHAw3bp1w8PDg2HDhvHWW28RFRXGl4t8kMk0NG1a7ddzuUp4oaGhHDx48MZP6jXyr5SFECKRaldZhBA/Aj9epk0O0OsPx2uANdcipJPfsViKkMlccTgUGI1GgoOD6dmzJwkJCWRnZzNz5kwOHz7Mhg0bmDhxIq+88gp9+/bF4XDw6quvMm/ePB577DGOHDlCXFwcGzdupG/fvkRHR3P48GEqKiqorKzkyJEjNctZ/4RCoSMwoB/ZOd9Tp85wtNqoGzwL/z8Qwo7ekIpMUuLqGlHb4twUysqSSEubQ0npTpRKb8LDHsPVNRK1OuCCgghEoXD7xzdvhbc3nvfeS/mqVdhKS1F4eaGJicEQGMiiPbvZd+o0hQiGDRvGjh07OLH1R5RJZcRFutG/aTsAXv5xHY2Cqx089ueXsGvjasLDwxkyZAg7dxylcb26THxuHHfffTcnTpzg888/58knnyQkpNqHR5KUTHlFhdl8mlGj7+Heez7Dz+/WzSr7j9xIv9yr/XHGWfw1Bw89LHbsvEu89tpr4qGHHhKDBg2q+a5p06bCZDL96ZrWrVuL5OTkmuPjx4+LO++8U9xxxx1i6NCh4quvvhJCCPHDDz+Izp07i0GDBonOnTuL9PT0K5bLZCoQiVubiaQDQ4TDYb+GEf7/xG63iIqKkyI7e7k4feY1sX//QLF5S0OxcVOU2LgpSuza3VMcOTJGpKbOEYWFm4XJVHBd7vvll1+Kdu3aiejoaNG4cWPRvn17ceDAASGEEB988IFISEgQdevWFRqNRtSpU0dERESIBg0aiGbNmgk/Pz/h7e0tWrduLb788kthMheK7OzvRb9+ccLVVSV0Oo344IMXhcVSXnO/i30mJCSIiIgIMX78eOFw2MShQz+I+PgA0bChWowaHSLOp88XNpteCCFEcXGx8PT0FEuWLPlXYzMlJ4uT9eqLwnnza8798NprYqinl8h4YqwQovr/jKG8XGzt0ly8FhUigvv4iXcH9xKv9+shVAqF2Ld5oxBCiOHDh4vDhw8Lh8MhGseEiVHNFWLvi83EQ706isLCQiGEEO3btxf169cXKSkpQghRc37fvs0iMtJNbNxUV6SnLxAOh+Nq/qn+EW5wnIUz3cdthr4qGWGsjsIOCQlh9erVWK1WcnNzL/h0qy9pn5ycjCRJNZHbUO0h9dtvv2GxWOjfvz933303AAMHDmTgwIHk5uYyYsSImqWqK0Gt9iMmehKnTk8iJ+d7QkIeuA6j/e9TXLKD1JQPqNKfxuGoro0gl+twc2tASMgQ3NwaYbWWUlqyC70hhcKijVxcllGp/HF3a4SbW6Oa6HkhbAhh/8PPhWPsCEf1sUNYsFiKKSrK5t131/LVV+MYOnQeVVVmFAoZXbp0ITExEW9vbywWCwEBAdjtdry8vEhPTyc/P59+/foREODHpk1bSEraz6hR+zl6zAdXVxm//FKAq6uE0SiYMOEdXnvtXRISfKmoUHPyZCmlpUaUSgWNGoXQtOlptm5rQf/+J5DLJEwmOWlppaxdM5XBg0/zwQcf8Pbbb9OhQwemT59OYmIiCxZUh3vNmzePhQsXolKpeP311+nevfslcytzrc40YPzDEo85NhYPjYaqrVtxWCy4u7nxw+h+xOcakQ/ohi55LxtaFRBxvh5ePj40ad8RgOHDh3PXXXfh7u5OUW4eLzyixkekcHi/HlVOEtnmxpw4cYI+ffpw5MgRoqKiajycWrXqio9PLGp1U86ee4vy8kPExb2DQqG9cX9YNwCnsriNsFrLMFvy8UzpRAdrPRrf24Z69erV5I2aPXv2JUtQUJ135n/9tmfOnFlTkWvixIlcdFUePnw4mZmZuLq61sRj/BuCggaRm/cT51Lewde3O2q1/zWO+L9PScl2KiqPEhY6Eje3Rri7N8bFJRxJutRRMSz0UaC6sltl1SkqK49f+DlBUXEi/yYEat1vlaxZY0CvBxdXifvum0pFhR293kHXrpUcO1ZJq1YtcTjAx8cdtVpJTk4xGRnpOBzViur776uTRrq4gLu7nPx8Ox/OLKq5h8OhQadTUF5eSVWVYPXqQoYNq8uhQ0bcPaC8zMKBA2n88IOFd997FB/vIs6cSUGvtxAREYGXlxeDBg0iIyOD3Nxc3N3dcXFxqem/oKDgbx0yhBDkz5iBpFYT8MrLNdd5e3tjjoqE4hIKP/qIrLMnaZLvS8p9rYgaeCcZ9y1nxjvfMO2BaYSEhNS8fI0ZM4Z9+/ZhNBrp1LYlh/PsDHphFlMzX+Tu+x8hpElnmjRpwunTp4mNjcVsNiOEQKPRkJ2dTXl5Be3bzefkqQnk5/9CSMgDeHt3uPI/lFuBG2m2XO2Pcxnq8pSU7BEbN0WJ0+9+JDJf3CZs5X9ecqpt9PpUsXlLfXHk6NjaFuW2IDPra7FxU5QwGrOvug+bTS+qqs4KvT5VGAzpwmDIEkZTrjCZC4XFUiIslnJhtVYJm80kioryRfPmzcXnn38uQkNDBSDq1AkRnp7uQqGQiw0bpgilUiaUSklQbcLU/MjlCJmMP50HhFyhEEiXXhPaILTmd0mShEqlEv7+/uLll58TSqVCuLhoREBAgEhISBAKhUKMHz9e5OTkiAYNGgg/Pz/RuXNnERsbK44fPy6io6NFz549hZ+fn+jYsaP46KOPxEMPPVQzB507dxZnzpypOS798cfqJahPP7tkrkpKSkTLFi3EkfpxYmP9+qKZq4tYPThBCCHEgFUDRPOnm4tmzZoJnU4n9u/fX5M2JzY2VlRWVoopU6aIO9s1Fp/11ghRmCzE/G7ijkb+Ij4+Xnh4eIjZs2cLIYTIyMgQzZs3Fx07dhTt2rUTmzdvFkIIUVl5WmzcFCUyMr+qkel6LUvhXIZycpEqfXV2lfCnBqAweiF3V//DFTcfV9dIIiPGkZL6PoWF6/Hzu6NW5BBCYLUWI0kqZDI1MpnqlnRHdHWJAMBgSEOjCb6qPuRyV7Ta6L/8/simTNKOFZJ9uoy9yiV418nnnXeeo3GEG5mZ4DDq0aqhrMxOnz7v4nBIBAQEkpeXh0qlRAgwm838TfVd7Dbbn85lnvw9eYMQAqvDint7d956exbCIVAolAQEBJCYmIhOp2Pz5s189dVXhIaGEh4eXnPtvn37iIyM5OjRo/Tp04fZs2fTqVMnrFbrXzpkyDTVVohLs6aXyOTl5cXYJ5/k0UmTERUVTA7whyGjeWnaSySHJzPpiUmc9jyNn58f8fHV+VGbNWtGRERETRZnT50XDzVRQt5Rhi9OwWIx4x/mz7fffkt0dPW/w195OOl09dDpGpCTs5w6IcM4l/IOGRmf067txls+EadTWdxGVFWdQaHwQK0JRHK59R58FwkLG0V+wa+cOfM6Xl7tUCjcbroM589/RGrarEvOyWQqtK4xNGu2CJXK+6bLdDkuPiAMxnS8uTHLEkc2ZVJZUh30RWkuwlBO48ZyBNUR9zllerBXl7JVNeyC+fBG8nNz0CjAYDLXKFlJBggQ/5uDQZIuc/LPqFxUCItAyASSkAgODqZFixYUFhYik8n4+OOPqVevHr6+vjRr1oz4+HiWLl3K888/j0ajQa/XU1xcjJubGx4eHtx333307duXgIAAmjZtSnDw78pWE1cfAOOhQ2jbtr1EjhEjRjC0Z09SevakKlhBq3sfIj3yU6TDEj3De/LgtD+Hgg0aNOj3evBWE8xtAZuns/jZnnB2Hbz4a/U8XAE7dwTzxRdfoVD48cQTGmJi1VRWnsTVNZJFixYxderUGmX59ddfExISwvDhw8nIyADgyJEjLF68mL59+zJixAjWrl1L7969r+je18KtXW3DySXo9ckXfMlvXUUBIJMpqV//LcyWAs6lvPfPF9xAoqLGUzdqIpER46gTMgy94SwnTo5HiL95Tb6JqNUByGQajIbzN+we7frXrfld42mnosKB0kXD7txW1eew1qShqDqTBEjYBVjsgAKEVK0IZH+lE/7npEatu+RYqaiuKy23yxHJAmwX9hTy8ykrK2Pp0qU8/PDDvPzyy3Tv3h2ZTMbo0aPp27cvnTp1IjIyEpVKhdlsJikpiffee48jR47QqlUrtm7dyuzZs3F1db3EIUMVHo62UydKln6N40J09CUyhYSgcrexNbOAVq2bM2XIFOqU18HP9fdUQzNnzqxJGx4ZGcmECRMAGD7yMbos09Bl5nG8hi7glyNFUJnL22+/TatWrWjdujUzZ86s6Scv72cOHHwQh8NMaWkpS5bsZdbsaF540Z0vvlCjVgeSdn4uDke1wh45ciSJiYkkJibWuOEuXryYxMRE1q9fj6enJz17VrvgvvHGG3z77beX/4e/zjiVxW2CEIKqqmS02n8OkrsV8HBvSmjoI2Rnf01ZWdJ1799uN/7tA99VW/2A9PXpSkTE40RFPUNMzGRiY16lpGQ7aec/ue4yXQ2SJMPVJRyDIe2G3SOq2e8PQFd1O86nKUnJDMRkViID/LTQPaL6e4XOExx2VPILeYwc1Oyd2+1cEtGv/ot3FpP50hxhVpsJSZIwGAxkZmRWh+dS/Tf9888/89JLL7F9+3ZMJhMnT55ELpezdOlSzpw5g6enZ01KjZdffhmHw8H+/ftp2rQp7733Hl27dmXUqFGXdcjwGTkSe3Ex5T/9xKJFi2jfvn1NWhzDr19SWCpjSXEpTWOsGCwGtry85RKl0K5dOywWCw6HA7VazaBBgygpKal+w9e4IzxCKTNBfLCcypT9LFy4kD179rBr1y4+++wz9Ho9druZEyefo6xsL1sSG/Dddw/RokUQLVsuoF/fX7Fa3QgLewG9/iyFRRuAasXQsWNHpkyZckniQYBff/2V7t27o9FUK+CLyuRm4FQWtwkmUw52exU6Xb3aFuWKiYp8Do0mhFOnX8bhMF+XPh0OK2lpc9m6rRm79/QkJ2dZjcvpH3F1rQ4MNBhSLzkfHDyEwMB7SUubTWnZ/usi07Xi4hqBwXj+hvUvV8pQaqqf0P6iLvHx7Tm65xSG1INoFFCgh23pDuQy8Ep4BEkuxyHAanPg1tyNPzpmXSw1qgB00l89PqotDW9v70uyIUuShKa+Bk0dDQqlAofDgUwmQ61Ws3PnTn777Tc2b95MkyZNkMlkPP/886xbt67GVfa1114jNze3xpJYsWIFW7ZsYfXq1TV7BX/EtU1rNE2akPrZPObMmUNiYiJLly5l3LhxlHwxn+N2Iy3ra3ipQR51p0QRERFBvXr1apabxo0bx3fffceKFSvIysrC29sbb2/vmrf+XkNGE+LnSZC/Ly4evgQHB2M0GjEajbi4uKBUKjEa03+XxzWK7JzjWG17OHduBi4uoXh4eGA0VD/4ZZKCe+65h1OnTrF161bS09P5+uuvLxnT5bwbbxZOZXGboNcnA6C7TSwLAIVCS7160zAYzpGR8eU192cwpJF0YBCpabPw9e2GQq7j1OlJ7NrdlYzML7HbDTVtqzeOJfT/oywkSaJ+vTfQqIM4m/wGQvzbrPvXH1eXCIzGTByOP28SXy+0HtXOELm6DDR3a+h+Z198JQVKOdT1umAiCOiUsxM3pZpAncSQRgqsKUaQQO2hwFsuu2gU4OqqRubpga9cjlapIDIolotJpnU6HZIkoVAoiI6ORqlQ4xsQhMJbQedHOzPigRE0bdKUoUOrU8y1b98erVaLh4cHHTt2JCkpiUWLFlG3bl3uvPNORo0aBVS7dv+dJfG/SJKEz6iRHEg5R5vQUFQqFZGRkVQW5lFyqhRbvRA0IRFEW6086t0cT50nKSkptL2wx3ExLc63335Ly5YtSUxMvKT/+QsWMHzUE/BCKoqoDvTq1Yt69eoRGxvLY489hlKhID1jHjKZig4ddtKu7Qbat/sYpSKeqqqTnDo9mfLycmSy8wC4uzfHy8sLuVyOXC5nyJAhJCX9bpWXlZVx9OhREhISruIv4NpxbnDfJlx8Q/47r5dbEV+fLmg0oVRdh4y0J06Mx2jMpFGjuQT490IIQUnJNs6f/5SzZ6dz/vwnhIY+Qp2QYSiV7mg0IX+yLKDae6hu3YmcOPkcubk/EBx8/zXLdi24ukYihBWzOeeG1QSxYyPT4zSJdZdVn7gPmiXUIfvDfJJGuDJrj4VXt5j5bfc66ihUdIuJ5WSlieAmJnKzKgixy8mv0BOoUBLXtQvh4eF8/fXXGO12ZEJGfn4KCpmcYO8omjdoy6ptiykoKKCgoBAXlSt2s402HdqQuySXxDOJ6PV69Ho969atY+TIkXh7ezNgwAAWLFiA3W7nqaee4v3336dHjx41Y1i8ePEVj7fgfCo7ly3FYbezN8iHzHOn+GBwH+LrNUaVmUN5cAihgx/l1PLZOGQKnun8Jh880Y5HHnmkpg9fX1+OHDnCkiVLcDgcl3hbFRUVkZGRwUsvvQSSxJlTp1m26Fv2PLcUqfwo9854hlDtW2jDNISFjkSjDgSgXbuOvPrqVMY+2Y3k5P3odDrkciMARmM6RqOyJuPs5s2bqVfv95WEZcuWMXDgwEvSnN9MnMriNsFgSEOp9EKp9KxtUf41NlslSqXXNfdRUXmMyMhxBPhXpx6TJAkfnwR8fBIoLdtPevqnpKbOJCPjc1o0/wZX10gMf7FxHBDQl+zsbzh77m18fBJQq/+5yNONwuVC3ieD4fwNUxZVPoWY86strz7hfcnNL+aA3y7iEjxIWFSOJJOxfYSWNWetzE+ykXg2myHdOrG+MA1jOxmKlnfwxNtr2VJVScaOnWzcsIGg4GCaRUdzOiWFCLmcKrmCtNI0lKkKBnd8BkmSWLX3c4ID6yDXCMrSy1i9ejVhYWFoNBpWrFhBgwYNGDt2LOfOncN+YYlrzJgx9O7d+xJFcaUYKsrZ+d0Sjm5eh0bnhqu7B15BQZw9dRqAA6eOUiEEHjIZjb0yee3QGewjx5BbARUVFZcoi88//5zHH3+cs2fP0rdv30u8raZOnUpMTExNLYvyTem4GOVIxXIQLZCpXCmRZNTz6kV09Es113l5eTF27FhGjngFh0PPwoWbKCquZOVKCYViFL/+0ppt246gUCioV68eb7/9ds21S5cu5eOPP75kvK+88gpr164lLy8PIFaSJK0Q4oYUlnEuQ90mGAxpNT75txNCOLDZylEoPa6pn+pNcoGnZ6vLfu/l2YpmTRfSqtUqJEnF6dMvo1EH1VTx+1+qi0S9jcNh5vSZKYgrcP28Ufwx1uJG4Z1gY1/orwD8mv4LB0zVNRV6t9Kx7rkG7Fg2h9A6Xvx02kbqs678vHwm6wsr2LHjFDlLc/CzPsyp0R8x7aW3GRUUhr9KxXfz5jHTaKLMYOCNgAC+GT0KN3cdazb9yKR3x3Iodx2h4SEs+/GbmgpyFz2WoqOjayyFlJSUmo3aiRMnEhQUxNNPP/2vx1hZUsTCZx/jeOIGWtzdj5Gz5vPozE95edE3GNx9GPL0c5QYjSg0ChoseRuvQ3MZ278jCe8d4t577yUiIgK9Xl9T46Vhw4Z07dqV1157jZKSkpq0OFAdwf7UU0/VHIdZfWnZsDkD1z1H38WP0TqkOaGhKrJK11B66vNL5BwxYgRLlgxg3vw2xMfH07pVVz54fxsymYb77sti79697Ny5k4ULF6JU/p7Jedu2bTRufGnp4unTp3PgwAGys7MBkm+UogCnZXHbYDCex9urfW2L8a+x2SoAcc0WUVnZfiRJiYd7s79t5+7WiNiYlzlxcjwWawlWawk2m/6yeXhcXSOJihrPuXNvc/r0ZEJDH0Wnu/l7QnK5BklS3tBN7q7Rnfn8zGdUVK94MKfrHFpv+QCtzgijqwtdHSoIpFO3+ajkO4kOcKOqqgqz2YxaraZ5mCe7UuzcOfk+1vz6A6b0c7Tw9SXjwvr6ax4eqHfuZODAgSxZsoSysjJ0Hq707t2bn376icDAQHQ6XU22Y7VazYoVK9i1axcxMTF07NiR2bNn89VXX9GhQwe6dOmCn58fy5cvv+IxKlRqbBYLcZ26ki6UvHzHHZQX5PPum9MZO3YsA555nsKM88SF+tBu2FOUFlbicE9jwH2D0Gq1VFVVMXbsWBSK6seiXC5n+/btpKenI5fL6d27N0OGDOGxxx5Dq9Xy2GOP1dzbXmJi6uOT8Oxbl7K5i6nKDkOet5yTgb+Rl74Y7wZjWLRoEfPmfYLDYeLJJz2Jif09fYlK5cfXS7M4dDgbF00XXn31Vbp168b58+cZMWIEZrOZ3r17M3nyZOCf82LdCJzK4jbAZtNjNufd8hGel+Ni/Q2lwvOa+ikr24e7e2Pkcpd/bBsQ0I+08x9jMKQA1TXC/0oJhIU+itFwnty8FeTkLkOrjUGhcKuJ+pZJKiSZCrlMjSRT4e7ehOCgQX/K3XQ1CCEoLFxHcvI0hLCh0944T7cAbQCb7v+f6oceP8C5jTWHJTmpeOVuhRgFWPQ1VReDgoKID/di1eEcssuMpBQVEKRxwZqbB4BKpeLTTz+ladOmdO3albp16/Lcc89Rp04dPv/8c/z9/ZkzZw7Tpk3jxRdfRAhBgwYN6NmzJ8OHDwcgMTERPz8/rFbrVY/RRedGZLN4Th9IYvHBk6z9aSWznxjBU089yVtDenB/dAjKmEAGt3EQUL4HcKPX3oYMGjSItm3b0qpVq0uKgn344YeMHj2aFi1acPr06Rq5x4wZQ1ra71agw2BFWB3ILzgRuD98D4a3d7BsWQXzz2YjSdmMO1qXmfOzmf1xMIXFNmbMSKZ3rzt45eXu2O122rULoqJCz/JlXzNt2krefPNNpk2bhre3N1OnTmXq1Km89957fPPNNzz11FPMnz//T3mxbjROZXEbcNH9zuW2VBblANdkWVRUHKOi8jhhoSOuqH21J447crkOu73qb5WFJMmpX386UVHjyc1bQVnpPhwOM3aHGbtNj0NUF3ZyOCzY7UZycr4jN2c59ePeRqeNuWyfV4LJlMOZ5NcpKtqEThdHkyaf4e7e5Kr7uyo86kBVPtgsoFDhLauizGCDJg9Bw/6Ul8+tqbrYKKR6GfF4djnJxYVYhANzRTk5Viux0dFMmVK9lBcVFUVwcDBFRUXUqVOHZ599tibCumPHjjz44IN/ynZ8PSk2C04kJxMaEY25ogIfnSsWm52M3EoUcjm9Q04TUF69UV149xekfT/lT95P8HtRsBEjRtS4xAohalxi/4jtQnS8wueCC6y7B2WmchYm7eCDL4IpKrLx6uQsWjVVEVpixj0qjuKiXCwWfzZs+IRDhx/m5cm/4u0dQKdOTxAdHc3KlSvx8PCgfv36dOrUiTVr1vDxxx8jk8mYNm0a3bp1+1OhshuNU1ncBlxcy76tLYur2ODW68+RkvohhYW/oVB4Ehh4zxVfK0lyNJog9PqzGE2Z/9hepfImPGw04WGj/7KNEIK8vJ9IPjudffv6EhExlojwMchkV56jSwg7mVmLSU2diRCC6OiXCK3zKDJZLfxX9KgDCKjMAa8I2oRreSXDhrXbNHKLqi5JeV8v0A1Jgm1JR1EAvcJi6Dt1KrbcPOZtWI9Kp2Pt2rXs2LGDu+++m7lz5/L000/z9NNP16S8/+GHHy6b7fiPm7Q9evRg1apVaLX/Pn33jrNFfJPrRoDFivHMYZa/Ub1k4+nlRagmh6a+Bup6VELCy+BRh2+35XD//b97wl30fvpjUTCFQlHjEutwOHjllVdQqVSX3NdWfFFZ/G71Hs47QZs6sdzRbSUOSwUvT25LjFcsDZL3IQauwuGIwGCQ07VrB1Sq01RW+hIXdyd+fnsRQtC4cWMeffTRmqA8lUqFp6cnycnJNG7c+KoLlV0LTmVxG6DXnwMkXF3C/7HtrYbVWgaA8l9ucBcWruf4iWeQJCWREeMICxvxr3JMSZIcpcITmUx93TaOJUkiKKg/Pj6dSD77JmlpsykoWENc/bfw8Gjxj9dXVp7g1OnJVFYex8cngXqx03BxqXNdZLsqPC7cuzwLvCLwcpUxtpWKhLvvRZLJ/5TyPtJHy7qfltLdN4DukfUY76PCXlbGT/v2/UkJTJs2rSYu4mLK++joaMaPH/8nMaZPn8706dOvaSgHM0oZvTiJ8OjmNG/szWcffUqWJhhTnUYoz2Qx8MMfUW+dDm2fgDrVCQK/HtOGpUuX1vTx+eefM2HChEsspOTkZFasWEFqaioOh4OEhATuvffeSyKnbcXVG0Fyb03NOaN7FTpFECLtDIrYFri7u5NfUgmRICEwGEy4uroyb34vPv74PBs3qOjduz+ffLKAuXPn8uOPP3Ly5EmMxuq+7XY7b775Jnl5eXz44Yd4e3v/ZV6sG4VTWdwGVFadxNW17hWt199qWG1lwL+zLHJyf+DUqUm4uzehaZN5qFS+//q+kiSjtKx6Hfd6BzKqVL40avghgYH3cOb0FJIO3E+dOkOpG/U8CoXuT+1tNj1pabPJyPwSlcqbRg1n4+/fu/ZzfHmEVn+WZ1V/2m2MaK5ixJTtIP99qaVZs2YA1PXXcb7zMO7LS0GYKjEkpeE7dizjn37qskrg38RFXC1CCI5nVzBi0X783dUsGdkGubURX3z0AZvC78XVXlFtIflFwn1f1Fx3pUXBSkpKcHNzq7Gw1Go1VVVVl8hgKzEhqeXIVL+nQgno2IbdB37EsOMobrHVLxIHzmRjbSbIzchAo9HQvXsLsrOn0qfP/WzZvJWkpCS8vb3RarXExMTQpEkTkpOT2bVrF+3bt6du3bosWbKEhx56iEOHDl11obKrxaksbgMqK4/j5dmmtsW4KqotC+myVoHNVkVW9tfk5/9MZMQ4/P3vJCPjC86eewtvr440bvzJVVcTq6qqTuceHHQ/wcFDrmEEf42vTxc82/xGaupMMrO+orBwA02bLMDNLa6mTVHRFs6ceRWTOYfg4CFE133hX1tZNwyPC2/H5ReW6S5GkP/Fkliolys7zhYht1rwzckDmQzPgQNugqCX56dD2Ty37DBCgFYtZ1TPGNZlFvPZpnMUhiZQuXACqBUkfvvFn4qCLVmyhAeHXJpd9nJFwfz8/GjdujVt27ZFCEHXrl0vCZSD6mUoufulS1Md+tzNyy9MoSq3N6UZGXh4eDCic2MSFi1D2jiC0aNH880376BzK6N9u2gSEuDkyZM4HA5mz57Nzz//zOuvv87jjz/OpEmTsFqt9OrVi+bNm6PRaBg7diw5OTlXXajsanAqi1sci6UIszkPN7eGtS3KVWGzlqNQeCBJv791Wa0VZGV9RUbmImy2MlQqX44dfxKl0gurtQR//140bPD+v9oL+F/CwkahrzpDvXrTbugbvEKhJTZ2CgEBfTl2/EmOHhtDfMvlgETy2TcoKFiDVhtDy4bf4+kZf8PkuCqULuDq87tlIb/wOHDYLrEsLhLu44rRasd4QZnoOndGeROWP/6KzWcLaxLe6s12pnx3pOY7zb0DUTz3MJIMji6aS1VxIRO//7Xm+5fueZLSH85S9ksKHndFICnljB8//rIW0h8D4/6X3cfzCE0rR/zPn5iXlxfDmg+i7yfvo17zObNnz0bxzSD611cw8bcdFJedYsgDc0lLC+J82loWL15MYGAg6enpjB49mvvvv5+YmBjGjBlDv379eOCBB9i0aRNr1qxhypQpPPDAzS9b7FQWtziVlScBbltlYbWWXvImnZe3ijPJr2GzVeLr252IiCfRaWPZt78fBkMqIcEPUK/e1EuUy9UQET7mWkX/V3h4NKNJ4085cHAIu3Z3Q5IUCGEhKmo84WGjkclU/9xJbeBRB8ovBC5etCjslssqi54NApj260n8DNVOC573DbxZUl4WXUB1jW0hk3D4qHF4KBkeFUCEtwvzDJWk/rgM3+++YIbRwL0tGpJ16jh14hoB8PHPX/LjN8vgG8iuyKNXyx58/PNC9h1NYsKECSgUCvr27VtjifTo0QObzUZVVRUTJkyoeVgv2J/BVEASsGjRIubPn48kScydO5chTXoxpElvQt7qiFR2nneO5rI+1cbq7j0YN64XL73kz4LP65ObW8GQIUNq6lSsX7+eyMhIZDIZ3bp1o2fPnmzbtq1W5vgSbmQZvqv9cZZV/Z20tE/Exk1RwmIpr21RroqDB4eLffsHCLvdKpKT3xQbN0WJpKTBoqLi+CXt9PpUkZ2z/LqVmKwt9PpUceLkC+LosaeEXp9a2+L8M7OaCvFx2+rf178qxDRfIf7m32DWhmTxSr+nxJr4BGE3m2+OjH+B3eEQp8v1otBsFYGbD4kP0nJrvvNbtVXoQsPEjIF3i0m9uooIXy/x/v29xSejHxJ2u03YKs0i88VtIvPFbaJrVBuxavinIvuN3SI8JFS0aNFCtG/fXnTo0KGmVKvZbBYffPCB6Nixo1Cr1SIiIkIMGfGECH/xV/HmixvE0Wd+FVqtVjz66KMiNTVVdOjQQbw2YKBoElhftGrRQowaNUpMSnAT4t1oIYQQc+a8IBo0UIumTf3F/v17hdlsFhEREcJoNIovv/xSKBQKkZCQIBISEkRWVpYQQohhw4bVnPP09BQ///yzEEKIlJQU0adPHwFUAItFbZdVlapf9ZKAbCFEH0mSvIHvgQjgPHC/EKL0Mtc9A4ymOiXl50KIWdeq4P4/UVl5AheXMJRK99oW5aqw2sqQJCWHjzxKaeku6tQZTkz0ZGSyS99cXV0jb0vX4P/F1TWSBnHv1LYYV45MDgUn4Z0IsBjA1fdvK7490yMGetycNfJ/QiZJ1HN35af8UgSQ4PX7vliz7FTK3F1RyGU1sRY2ux1DeRljOvfkUFYRkk0w/cO3yV1Vzl1vDSVtcRJZOdnUCQjBZreTlJTEk08+yUMPPcTEiROxWCyEh4djt9vx9vYmRdeI7DkP8JYMpppMWO029u3bT2RkJKmpqby1dy9BWn+OTHiV8CceZ3jXOKLf3k/GyyrsdjstW6oZ94yap8c9wcTnqws/XYxih+oss4GBgXz00UesWLECh8PBuXPnsFgs1KtXj+joaPr27cuuXbvo1q0bVKf7GH7D5vtftH0G+GPq0JeATUKIGGDTheNLkCSpEdWKojXQFOgjSdLVRzL9P6Sy8gRubo1qW4yrxmoto6LiEOXlScTFvUO92Nf+pCic1CJx/ao/jaXQ/CHo8XqtinM1rMwvxVspp6mba825dljw/ENxLI1SgcFixWCxsu7kYZYNmsWC1+byxIvjuP/++1GF6EhppMfHzYtXm49hzYzPEEIQEhLCnDlzyMjIoF69epw8eRJ3d3eyC0spcAnH39eHopJiAr1D8dN54+nmD0BYWBjNW7QAyUrpyQocDgc2bRCFesF9d7YmIsIVo1HQvdtsjAY7ixcvJjo6mvLycl544QXCwsLQ6XSEhYWxcuVKTpw4UTOWiwWQJkyYwKuvvkrjxo0v7svVkyTphq0NXpGykCSpDtAbWPCH0/cAX134/Svg3stcGgfsEUIYhBA2YCvQ/6ql/X+G1VqB0ZSBm65BbYty1TgcFtTqQFq2+J7goPtqWxwn/0vCC9B7Jjx1APp8CE0H17ZE/5o0o5kmOlcUst8toqrKCkxWK5LGhcGvzUDl6U3zLt3JKC6lXkAwkTO60HR0F/Lz82sC88oqyxkw+D7e2vwpvYc/giQEGo2GTp064eLiwr59+wgODqaqqgpbnXi61fdHo5TRvGkTbEoz5aYqQgwubJ26g8LsUjq26052RTFtZ00mNiIMu0qHTiOx/2QSWVl6yss9+fXXIlJSUvjtt9/YuXMnJpOJL7/8knbt2jFkyBC++eYbMjIy6NKlCzZbtbfaV199RUpKCrt37+aOO+5gx44dFzP0qoDFkiTtkCTp5es9z1dqWcwCXqCmwCIAAUKIXIALn/6Xue440FmSJB9JklyBXkDo5W4gSdJjkiQlSZKUVFhYeKXy/6epqrq9N7cBmjdbRJvWv978VBZOrgylC7QaCb63V52UP5Lg7cae8ipM9t8fTyEREaQVlXLHi6/j0Lnj4x/AveMn4e1dF5nMgqXSRHJyMnK5vCalibe3NwqrnaX3v0+PsKbIhMDv0GHO/7aVtKwihBCUVVRhsdnRNe7Ga30bsHXrVkJCQujf/14sdgvrUncxZt4oJL2JTct+JNgtkB1jvqcwM5v1P35DbqWgtNyGj6eaoqKymuUtLy8vVq9eTVpaGuvXr6dly5asWrUKi8WCTCZjx44dKBQKysrK2LZtG/369cPhcLBq1Sri4+OZM2cOVJco/BEYL4R483rP8z/uWUiS1AcoEEIckCSpy7/pXAhxSpKkd4ANQBVwBLhsOTAhxHxgPkB8fHzt5Yu+haisrDY93dxuX8vidioD6+T2JMHLjQVZRSRV6Ol4Yd9C7bDSvm44w4c/jItaXRONnlxWhNFqZfe8xWwoOY+7u3uNsmjTpg2jRoxgvyKJY3mnCPH1JTQnm1MVlTRrUI8qsxGHxYjaw5ffpj5IuI+WU6cyOHPmDFu2bEEIQXlVKQ7Jhr9/ILll5RRV5NNz4SNoFBoKjQZkkhWzQYHeYcZsEYTXUZCTr2H16tW0atWKhx9+mLNnz7JgwQKSk5Ox2Wy4urry6quvAtUFkNRqNUeOVLsJb9iwAYvFQsOGDTlx4oRE9YpPsCRJ44UQh6/nPF+JZdEB6CdJ0nngO6CbJElLgXxJkoIALnwWXO5iIcQXQogWQojOQAlw9rpI/v+AysoTqNWBVxXB7MTJ/xfaeepQSJBYUllzrujMSRo0bUzSvn3s3LmT+Ph4mjVrxrylX5FRUsW+pJ9Rnz5OWGhoTXS2l5cX06ZPp9JUhoTE8rVrUYwZx7aqKuQKNfF9hqNUafBWa8jckM4v8/eRurOKN8d/RoBPMBqVC77ufiy9513UlYKQBg0RkkRmZQHlMqgb1wSHQ6JtcDz3d0lACDiXVsWmTZvYsmULSqWSffv28e2331JVVYVCoaBp06Z8+OGHpKenU1lZydKlSykuLqZ///7UrVuXY8eO0b9/f1avXg3VL+IvAM8BX19mqq6Jf7QshBCTgEkAFyyL54UQQyVJeg94GJhx4XPV5a6XJMlfCFEgSVIYMABod31E/28jhKC8/NBtvbntxMnNQKeQ09pDx8biCl6peyFIUCZHcjj+VILUv04wr8x4i1lvzECvL+WzjxdcEt09YsQIji5cg5fCh5YtWxJbN5atny1gdWkBR37+khCdP7nFBXz94WuMbfsgI1dOpsRQTrmpAheFmipDBU/+MpVScwXe583IZRJ2u42y8lLc87WMih/EosM/4jjvQOuqxGS28tHcmVRUmggODiY9PZ2cnBxsNhvr1q1j8+bNHDx4kKNHj1JSUoJKpcLd3Z3evXvj6urK2LFjOXjwIO+99x5jx449JYT4FECSJL0kSV6X81C9Wq4lKf8MoKckSWeBnheOkSQpWJKkNX9ot0KSpJPAL8CT11P4/zIGQxpGUwY+3p1rWxQnTm557vBx57TeRIbRDIBPaDhafQUZ5ZV/ajtq1GjWfrOSp7q3R5NVSLNmzWqC7wAmdr6XEa0Gs2jRIu68qyfb9FV0jmhFSGAk3y3bRFyjxmzM38fzx+bgUTcQuYeGbj16Ele3Hk0bNqbEXIHJaqZuw+bUq1cPtVqNXC4nIz+LFiFxrFv1C66urjSoH0lAgJxffv6RtWvXUlxczMiRI5kzZw5arZannnqKRYsWsXfvXnr06MHs2bNJSUlBLpfTunVr9u3bR/fu3Zk1axYjRoyA6vAEJEkKATyBsus5x/9KWQghEoUQfS78XiyE6C6EiLnwWXLhfI4QotcfrukkhGgghGgqhNj0V307uZSi4s0A+Pp2rWVJnDi59bnDtzpLwPriCgB8L+xD5JRePnV3SLvGhPk3JGnfLxxZsfqS7xxGOWVVWbw3fTq9u3RhTLuH2JK6h67hzQg7VI61qBIPlY5Vs75h+9ZE4iJieECXwKEzRxkfNxSdVkdwcDAOQxlZWVlYrVa6dOlCcEgwb+7/nJ0H9yOTyRj68JM0i/OmrMKEm07Lpk2b8Pb2Zvr06ZSXl1NcXExeXh4nTpzgzJkzjB49Grlczv79+/H19WXdunVYrVbuvfdeCgoKAOpLkrQdWA6MEeL61gp2pvu4RSkq2oxOVx+NpvZy7zhxcrsQ5aom2lXNmsJyRoT4onSrDmI1VZRftv3ixYuZu2UtJXlZBDXuRlN613z32ZHVLDm2mTJzFR/Mn0+7qJboLUZWn9jCd0k/Y7KYEcCiVz+hW912pJ48yyajF0ariYeWPY8DB/UD6lNUVITJZEIIwf59+9C4uJBXUMC8efOQy2W4leSybksRDgd4eqmYPXs2H330Ed9//z2PPPIIJ0+erEk78tFHHwHVy9OPPPIIarWaOXPm1GQEDg0NBTglhOh0o+bYqSxuQazWCsrLkwgPe+yfGztx4gSAgQFevJOWx/2HU+irqq4tUVZWhsPh4PyZ05w7coiq8lLKSkuZNvdTxve+i7xsX16a9yHJtgru69yXhu0bMKCrJ6e92lFcXExuUQ7BzSJQp+ylqKqEVq1aMX78eAYNGkRJhJ1VJzfSp343tp3fT6xvBGbJhsFmIj09nei60Tgu7JtU6fVE1a2LyWAgNzeXAA83Rr/xDg4Bbm4yvvjiUY4cCeLtt99mxowZvP/++/j6+lJSUkL37t1JTU0lKiqKvXv31hRpevDBBy8J1rvROJXFLUhJyTaEsOPr2622RXHi5Lbh2fAAfFUKpp7NpuX8d1EBeRvXMOuLjxAmQ02707kFhGrVWPSleIWEYzlwEofDxMHjM9g9Zw8LDuSTavLA19eX4qIS1NFqbHIbdpkdh8zB5MnVFfgqlWZ2l+9mYp+x/DhrPRa7lRJzBXX8g3n8qSf46aefiI6OplGjRhw4cIDi4mLkksSb9/ak0mThBGqOnErCaCghpE4AQUF3MW7cOKC6ah9Ux3707NmTI0eOEBUVVXO+adOmaLVaSktL8fL691UorwansrgFKSraglLpjbt709oWxYmT24KKCjO7ThbgopS4L9NGC7/+1JG5sSftZyQXNdGduuEbHEJQRBSJu/cQnJ7O89OmAfDL6c5ow9bj7lfClAN57FqwmEWr3+S5T04S0yeOsipwt/tCjAOD2UBxcTGurq7odDryCvKZtmImL4yfyOL5iwj0DKZr4w7IZDKEELRt25bly5djsVho2aIFNkMVYXGNmLz0O1xd9ZSUlNOkcTjhYaNYvvxnoqOrgyPLysrw9PTEYrGwc+dOHn74YcxmM+JCVHl2dnZNm5uFU1ncYghhp6g4EV/fLtecptuJk/86Dofg2JYsdiw/iwPBB54mOqCgoaY6KWW9pn3oMGHAJTVNzmRkcuTYsZrjnIIcyj0lTp0yE9PKFdeEO9EXf4H9kxOkHUwh51gOdzR8iP1nN1OkKkav1+NwOHA4HDz44IMsWLCA79b+wKHck1iyrJw4dRKVi5omTZpw4MABfHx8SE9P50xyMi+MGUlFiI2CghxiYl1QqzzIz1fSo0dvlEplTXXBwYMHU1VVhdVqZejQoTRs2JDMzEzuuecetFotdrudefPm3dRqi05lcYtRXn4Im60MXx/nEpQTJ39HcU4VW5acJj+t2gPKrJDYNrEr504Vwq8ZAGgl9z89UNu0acMrr7yC2Wzm5W9fJteRy1p9JHdXVqBzk5G4qxWuhWZkShleHb0w77WyJ3k9eaXn+WDm+yxevJhjx47h6+vL/PnzKSoqYm9FBUqZApvDTphnMAn39mDjxo24urry6KOPMmfOHMxmE+/N/4jgYEF0tBf+fo2pKE8nLS2NJUuW0Lp16xoZ161b96fxhoaGcvDgwRs4o3+PU1ncYhQVJyJJCry9b5hTgxMntzV2m43Er5NI3mdE5aKg54gG/PzVSUw+KsJ8XHFvFkjZr+kokFCXgdViRan6PdOxl5cXY8eOpU2HNpwtP0vsQ7F0ON6BD+Z/hk4B7dtp2ZekR6aWKNtZRmzTxuRty0Xg4Nlnn0Uul1OnTh1atmyJu7s7KpUKrVZLgIsPkl1wviKHtWvXUlpaikwmY8WKFej1lQhh4+GHfSkurse33yYiSXImTJjA5s2bL1EUtypOZXGLUVy0GU+P+Nu2foUTJzeSwvQ0lr0xHRQPERChpveTrXBxU8Hikxw8vIb27V9AkiTG1HuUovxUvjn8M44f5HS5u3vNW3l6ejrh4eG4a92x59k5Ov0oJ2QncGvmgZSu57nncvAOVGKrtINMIi8/E5lChUwmQ6PR4OXlRVhYGMuWLWP69Ol8+umnHDp0iO9/XkGfPn2QyWSoVCoGDBiAyVTF61Mj+eD9LOQKP/r0mcKKFYk4HNCtWwInTpz427KttxJOZXELYTRmU6U/Q0z05NoWxYmTW4qi7DPsXfETp3ftAEmHxgMCIhXVigLQmyvZs+8HzqcfJzs7m16d+yCsVtYM+4xkvyKm/zyHHTt2ANCrVy+mTJpEjFpN87u74CJXEN2kIbtVaZhNVhwOKCywoQpUofJRIUUImrZvzpHvLJhMJgwGA+Xl5ezevRu1Ws2BAwcoKiqib9++FBYWotFUu+0ePLgLF9dili7dS2FhBD/9tJsdO3YDIJfL2bp1K8XFxbRq1Yr7778fDw+Pyw/+FsGpLG4hiosTAZwus06cXMBsKeL8+U/YNGsb5gpXWvcbiEdgPbYvM15S0C+j4BThdZqgUqmIjIykzFhOz7BWyOQygnV+VFVVYTabOb39CGeOnsRl/AQKKyvx1AtOWSxUHj6GIlpJbH01BQU20MhIT7VgybdgLbayz7GN8oJyHn/8cVauXElaWhqVlZWcOnUKnU6Hv79/dZ0Lm42ysjLefLM7pWU7mP5GFW66ONav34ZCUa3YLm6Oy2QytFotGo2mRsHcylxLbign15my8v2o1YH/ifKiTpxcC0IIsrK/YffubmRnL8Vhk/CLUdHpwUdQuVyoiCf9ns3CYK5Co/m9rKpSrUKjUKFAjsNgw8PDg7MHT7Fqxtf0C++KqyqIrM6dyHHRIFcoqLLbMZ63cN8gTzIyrKSfMeMa44omRINA4KOS4XA4mPfZZ2i1WpYsWYJarUYIgUqlIi4uDq1Wy+DBd6JWS7z44o8s+16FyeTgzJl07rjjDrp06UJhYSFGo5EuXbrQtWtXunXrxpQpU2oy397KSNc5fch1IT4+XiQlJdW2GDednbu64O7WiMaNP6ptUZw4qVVOn3mN7OyleHt1oF69qYzpfS97zmXjH1aP8aPGkbfXjxzFFrYc3ERubi4VpRasdgs+vjoGDBjAggULkVsEVocVi92KUq1iYpdRvL3uUyRJIso7FJ8AHbtOHuOnn37ivv73YLZVF09SKMDDQ06Hjj4cPqYh43wGkqM6S9+Lffrw2Y4dCMBgMOBwOIiMjKRhwzi6dXehXr19jHksn6CgerzxxnsMHjyYEydOEBAQwCuvvMLatWvJy8sjLi6OVatWodVqr9ucSZJ0QAgRf906/B+clsUtgtlShMmUibtHs9oWxYmTWsVkyiE7eylBgQNp1mwRZrMnW4+fZ/LDCSxdupS3PpyBwVzJD2uWkZiYyObNm3HVeOCu86FevXp06tSJutHRmGxmfnvkC1qGNERYHXy56wckhYyfP1uCVulCWX4xQgi6dOnCy6NH16zJt2jRGhcXLwry/Xhq1LP4eWhp4i/HXalgQsxWdAobkiQRGhpKUFAQHh5qbLb91K+/jzp1huHjU4+KCguTJ0/mjTfeICAgAIDp06dz4MABsrOz2bhx43VVFDcD557FLUJF+WEAPNyb1aocTpzUNkZjJgCBgfcgSTL27t1L3WBv5JJEZGQkeqORc7nHaBrXrGaPwma30LzxnSRu+5LKyko++fgj7u7ak/u+HUeZsZw2oc04XZCCr7cPvk2i0FuNFFeUEh4eTrdu3ZDl5qKQJJq3bMnRo0epWzeaYyczyTn3GVaLC1UY0Kih73ID3Tp356dNe8nMzKBV6wiGDSunUcO6xMW9wwsvLOThhx/l6aefruVZvP44lcUtQnnFYSRJ4Sx25OT/PSZTNkBNxuWSkhK0aiUXl8zdtDrKqgpxD/l9j0Kj0mITFiZOnMjUqVPJPF1EXe8wopvE4+lwsOHUDqJ9w3mq7TDu7tWL8rJy4t28Mbi7s/r5iRydNImnAgKQKxTcP+JJtrp1JQIDA84bWLLlHZp4l/F0axVdIxUcpwRXz05UWk7QqbOBd98xc/r0GiZNmkZQUNB/UlGAcxnqlqGi/BA6XRxy+a3vFeHEyY3EZMoBQK2uVhbe3t4YzEaslup045X6Kjx1flQZfi9sZDHrOZW8naFDhwJwen8O79z1Aqt3rOS4PpWImEjkkpyX1r3Pz4M+Jso7lECVmoYC7njkYV6sqmTcK69w5swZdpzPB+A++TbIfYwnmqRyNN9OwsMvY514EvXQAezYv4nuPdwY0H8J3t6RbNu2n1mzZrF9+3a6dOnCoEGDbuaU3RSclsUtgBB2KiqPERQ0oLZFceKk1jGb85DLXZHLqz2E2rRpw9msCsxGKxkZGWhUKqKDGvPR6gV8Pv4b8vIl5JIMuU1GTEwMAEItRylXUDcqls2bN9OnTx/yLGXIJAm9xYhW6YKvvZyA/DymP/gQP730GjMWLwG5HP24Z1GeLses0PF2Vw2fZtdnQKNWZMUEkXagHwZDGV8tfoJGDadQUKCnrKyMrl27YrVaa3PabjhOy+IWoLRsH3a7Hk/PWz/k34mTG02V/iw6bb2a48rCJNpHRTBzxVEeeOABnnnkMUoq8/HRhfP2N2+yaNOb1PGJJjKiGe+99x4AdrWcV9Z/SEVZCXfeeSfPPvssTz/+FBISfRaPwaS2c0iYKWgezomEKia/PpXMj2dSrjdQOnkslhgXfmh0P0kPbmTxcQONW+7l7NnpuLs1JCL8c0aO2EK3bn0ZNGjQTU/oV1s4XWdvAU6dmkR+wWo6ddyLXO5S2+I4cXJ9sNtg12woTQd9EZxZzaJUf+af80OSJObOnUuLFi1qmk+b8hYbE9dRpT9GWpoRFxcvIiMjqR9oY/2WMwRFhSNXupKTk0NxUTE6nY6iomJc1W60b3Y/Fh2cOrYKk8lEZWUl3hoP6sbFYBU2sjOzqNLrCdT4cG+DnuwwH6WwqJDKqgxKS+30adeGYyV6jKUlFPgGoUg7x8LP36d+7E5emvQDSfst3HVXD5Ys+fWWVQxO19n/OA6HmYLC3/Dz6+lUFE7+W6QmwqZpcPpXKE2j1CiYsymDxMREli5dWlPo5yI+hW0Z3PA13pgegdXq4Pvvv+fzzz9l8S+HaP/QUKZ+sZTk5GTmzp3L7j27KS0rpV5Ic565/3m2nP6FpNI8HJ3HcvJ0MiqFhk/7TWXxvC/Jzc1lzvBp7Bu9HLPNTFznptwX4cejve7EbtcQGe6Oxmzi8Qbh9Jgzn5NbfsPHXY6X53uUlR9k2tTXWbZsDRpN0C2rKG4GTmVRyxQVJ2KzVRAYcE9ti+LEyfUlfQfIFPDsMRi7m73F7nRqUb/G3fViCg4AU9XF9X7B7t15yOVyOnXqxG5bNnaNCxsatOWBtVsRLq4EBwcTFxeHJCQKy3MY/sy9SAoV5szjGFMP0LZ9B+w2Kw2imhLdpB6lpaVsLTvM0GXP45BBhqIIgMLkk1RVWghzCcEzIIghU9/l/c4uLPs0gcaNrYSE3E27tutp3XoCMpmztoxzg7uWyc/7BaXSBy+v9rUtihMn14VFixYxffp0CrLPE+WjZmGfM7Ro0YISm4rTGUXUrVuX4uJiKioq8PPzY/To0Qzr9ySLt7zD6awkDJYSVColpaWlvPfxN4iqSkrHDgNJwsvXl/j46pUWuUxFub6YVh070T7+Qbbs/AyPjg/hoXNQkVvA5oJDxCk6YTKZ2LBtEwVlebh4aDl37hz1Q6L4ceNiWoaFEBvfmhYdmlNkmU3R0Y2sX1/Fc+PfolHDp2p5Jm8tnJZFLWKzVVJUvImAgN7IZE697eT2p7S0lJkzZ+Lm5sb+2Q+jEGaG3H8/7du3Z/r6fHxcZaSkpFBaWopKpcJgMDB3zke069mYfckbsNmteHspkctlhNQJ4cznX4MQaDw8QSantLCQsLAw2rVrh8FciburNyqFK1u2fQR2G4Yjv3F82xosdivvLptBj+YJCCFwMcgRSonCwkKys7P5eu0GssoqsXh4s377OlJS36W0dBd+vk+Rnu7CIw9PYenSpbU9nbcUV6wsJEmSS5J0SJKkXy8ce0uStEGSpLMXPi9bNVySpOckSTohSdJxSZK+lSTJGUhwgYKCdTgcFucSlJP/BIajhbz8wBNknD1Lfn4+ev94zDZBZlYm69atI8xHy7d7snDXueGpdcdsNuPp6cnYfm9gshoAgcFcSUGhFb3eQv2e7UFd/bhwSMCFZHutW7emrLQUAJkcXDzkhPnGEuJTF3PGcQQSdrmCFxMe4+k2Q3Fz0RF/Z3t8fX2RyWS0atUKtVqNSqVArjiPwVDEih/MyGUz2LNHi5+fPx06dKilWbx1+TeWxTPAqT8cvwRsEkLEAJsuHF+CJEkhwDggXgjRCJADQ65e3P8W+fk/4+IShrt709oWxYmTa8KUUsbZ979j1aYVtJQr6Ne8LU+/tRAAT62ac6s3k1PoQpCbJ6GugVQaq/DUuNO/f39+2PFpTT89OvUGQKmUcWjVJrBV72VYysrAoAdgx/btpKSmopCrKCrPJyMzHQ+tL3c0ewCbxQgOOxa7lcnrpvLMyvGotRoWL15MSkoK8fHxvPzKIxgM6bi72zGZHLRs2YEpU2bRqVNvFixYgL+/f81SF8Arr7zChAkTWLt2LT169ECv19+kWb21uKK1D0mS6gC9gTeB8RdO3wN0ufD7V0Ai8OJf3MNFkiQr4ArkXL24/x3M5gJKSncTETH2/7WHhZPbmxNVRr746Tjjj5uYve1zqhwODhgNaI+nUGbSk1Ohpsqip+fIR0CAxWFGSOUIwOKwsGvXLrILztf0p5V7IZdXKwuz2QF2OwC+gYEUlZaB2URpcTEDWzbiWI4Fb/dYUksOUaTPRy/ZkevcGPdAT2YGb+BInowHVxp45MkJGI1GFAoHQ4epSEq6h/feD6VF8wm4uNxFz5530b599Z5h48aNef755/n+++9rZJo+fTrTp0+/eZN6i3KllsUs4AXA8YdzAUKIXIALn/7/e5EQIht4H8gAcoFyIcT6axH4v0J+wWrAQWBAv9oWxYmTq6KkZBfbk4ZyR1EaZaZK1uenMcDDEx+5nHVZJ0gtyaTcZMRNpeXpdoNQK1SEegRjspoAsNitnDt3rqY/tcKFbUnrsdtBkuxovFzhQhxYcWUVmKuvqxcagkKlokqqYveZNWjlPjQIbklh5WkQerpGbAegaaAchVcdxo59jHXrlhHX4Bdyc38gtM5wet29jdDQh/H1DaBnz54cOXKEY8eOIUkScXFxN3kmbw/+UVlIktQHKBBCHPi3nV/Yx7gHiASCAa0kSUP/ou1jkiQlSZKUVFhY+G9vdduRl7cKN7eGaLV1a1sUJ06uivyCX4lxHKa8wcccyjlJfa07DpUL/YLrYXHYMdks6NQ69A4ze3L3UWIsRyEpqDTr0SjU+Lh7o1AoUKmqK8h5eLtTWlUAgMWiwFf7e5lRcWEJSiaT0bxzF+547Gm6duvGa5PfICX3GMkZ63A1ngI5qB0CWo8ha+geqmx2Nm26F7Mll8aNOtGm9VpiY6dwcSXJYrGwc+dOYmNjOXDgAGfOnOGuu+5i6dKlvPfee+zbt+/mTuotzJUsQ3UA+kmS1AvQAO6SJC0F8iVJChJC5EqSFAQUXObaHkCaEKIQQJKklUB74E9uBkKI+cB8qI7gvqrR3CYYDGlUVh5z1tp2clujVgchBKhMBtKMRZSjZEdxHp46Oa5qN3xcdAxq3ovlZ3/jcGYWJpuZrIo8BKCQyXnkrgd459s5QLUSKCioTuDXsWNHDh48SFZW9Yq1UqnELgQS4OXpybZt29i1cyeFuTm4q1UIYaewSs+GE2fx0Mi4981KfP2W4uW1kLFjVWzYWMLDwx+nadNZNbIPHjyYqqoqrFYrQ4cOpWHDhjRs2JBHHnkEgNdff53o6Ghat3am4LnIPyoLIcQkYBKAJEldgOeFEEMlSXoPeBiYceFz1WUuzwDaSpLkChiB7sD/nzwef0Fe3s+AhH9A79oWxYmTq0ajDiInpz6pKa3IkWdxWl/G4NYPs/roTxjMlYxuPZTUwnPIbXZ+HP45/RaPoqCqDKVKyevdxnGs9DxBQUGUlZXhcDiw2Wx4enpiMBgYPHgwGzZsIDs7G51Oh8FgwGazMfaR4Zw7e5YNmzbjq3XB5nDwQJumdG/Tkn4jhqKNrUdq6kxy835EqQwhKuo5goPu/5Nr+rp16/52bK+//voNnLnbk2tx7p8BLJMkaSTVSmEQgCRJwcACIUQvIcReSZJ+AA4CNuAQF6yH/68IIcjLX4WXV1s06sDaFseJk6tGpfZFIbcAUCUAJJq0H8IJSy7FSb+w6vQmlFjQqlQYLEZ8XL0QEpRbTSw/vpaDuSfR6rS4urpisVgwm814e3uTlpZGTk4OJSUlKBQKKisriYiIQCZJvDt7Di5KJRqlgt5N4oiuE8w9z79CcP1I0tPncXTPWIQQhIWNJDLiKRQKt78dg5Mr518pCyFEItVeTwghiqm2FP63TQ7Q6w/HrwGvXYuQ/yUqKo9iNKYTEf5EbYvixMk1oZDrcNVW15iwCTORAfWZtfgJ8ovPo1Soad/gHn7Z8ykWm4W7F41Co1RTxz2QaJ0707s+Q+/FoykrK0MmkyGXy5EkifLycmw2GxUVFQghEELg5eXF2h+W0bVrVwLd3fB303IoM4c6HbsydsoUcnOXsWv3SKzWEgIC+lE3agIuLnVqeXb+ezgjuG8y+Xk/I0kq/PzurG1RnDi5JgoL16NWV+8Uq9wFroGCnk1C6RDXC5WbB3tO/YLZZiXEPYBVkz7k0cH3ca4knXeGTGbJiZ+RKxTodDrWrl1Leno6DRs2ZMCAAYT6+RGi1bLlp5+oFxONxWigU+cECiurCPDQUbdxE3recSdafwv79vfmTPJraLUxtIr/kUYNP3QqihuEM8fETcThsJFf8Cu+vl1RKt1rWxwnTq6JktJMCgsjAAgJCeHXX3/lrACbVY5KJkeoZAggxCuIA7Yith4/gtFmpvfHjyKEwNPXi7y8PIYMGUJsbCzHjx/n1KlTKAE7gm733INdCFQKOd2b1OfHI6cY9/p0Vv36PSGeGWRmJtG8RVOaNJ6Hr293Z7zSDcZpWdxESkt3Y7EUOdN7OPlPcOxoQ1JTWqFQmJGp5AghkClk2ISZSlMZA9s/iUySOFFwmomvv8Rdd91FZGQkzZo1Izg4mA0bNqBUKgkLC6OoqAgvLy9WrlxJ3QBfPF1ciPTzRgLkMhnl3oG0iG/G4m+mc+ddR7FaSggKGkib1mvw8+vhVBQ3AaeyuInk5/+MQuGGj0+X2hbFiZNrQghBUVEVTZo0Jjq+D5lpGdSvX5+xvXrgogAPT3dGvdmNAB93LFYzZruVadOmodFoOH/+PG5ubnh6eiKTyfjqq6/w8vKiZcuWBAcHo3VzQ6NS0qNZQwb1v5fud/QkoZsbpWVHkcsKSOj8LKGhD+Pt3QGZTFnbU/H/Bucy1E3CbjdRULgef/+7a2oLO3Fyu1JVVYXJZOLo0WPAsep0Gi6enMzMxmy1oq/S03/gAF55/jmefOllJr/wAloPD1JSUlCr1ZSXl3P//ffTr18/2rVrdyEdh4IuXboAgjsSEmg3eBCPP/44VquVbdskRo7swN69Zp58cgPnzp1Dq11DbGysMxbiJuFUFjeJouLN2O1VzvQeTv4THFv/e9ae0PQMjhw4yLGCfPbb7dwVV5fNZ9Lw9PSkY7t2yCUZn3/5JTKZDLvdTkJCAmfOnOHQoUPs3r0bHx8fADp37kxKSgru7lp+3bCen9auxdVV4qGHGjB37jLc3BrU3NMZNHfzcS5D3STy8lahUvnj5dWmtkVx4uSaCdQb0FVW8ni/fjTo3oWTebnoXDQMGTyYQ9n5WG1W9Ho9B3buIMjTnZLiIvbv34+rq2uNtVC/fn3Cw8M5duwYAQEBNGjQACEMPPuckfhWavz8NNhsCs6f92X9+lOX3P/1119n6NDLZg5ycoNwKoubgNVaTnHxVgID+iJJzvKMTm5/dIUF7AuOpVm5jDnLl9PaxZU6/hq2bNlCflkFHetGcDY5maenvcWYQf05cfIUo0aNQqPRcPbsWUJCQnjooYeYPn06rVu3Jj8/j9mzP6S4OI/33yuhvCyEwkIzjgupS6dMmULphRoWTmoH5zLUTaC84hBCWPH17VHbojhxcl3YpHZjW8PqJaCkkBjauR8msllT2vpF8cuKH+gYG8Hp/ELGdW/PgIdHENOsGffddx/FxcV8/fXXLF26lJiYGGy2ShYsmMbYJwNp0sSVd99R88wz04iMjKJDhw4sX76cHj16MHToUM6ePetcdqpFnJbFTcBkqk6I5uIaVsuSOHFyfTjarCUArvZS9KFepDeOIiKsIStXrsRos2N198YnIJDhM2YR06a6VsTXX39Nhw4dkCSJunXDyMj8knXrO3Hs2Gl6dO9Lh/a/4etbH61WxwcffIBOp6NNmzbYbDZOnjxJeHh4bQ75/z1OZXETMJlykCQFapVfbYvixMl1QRsUhEqSEFixNUzgfFouKpWKuLg4Kquq2JqWzcKlX5NfZeS9994jOTkZSZLYvn07ffs2ZveeOzh7djq7d2lwdQ1h3Lij9OgxhNDQUFavXk1wcDALFy7k7rvvpkOHDowePZqAgIDaHvb/a5zLUDcBkykbtTrIuV/h5D+DQpKwCMG9nnZ+krtT9562zFsyD1eVK1u3bkWhULBhwwYmTpxIs2bNePXVV7n33rZ07XaYqqqTKBQNaNZ0EVNff5kff/yFxo0bA5CUlES7du3o0KED27dvJygoiOXLl9fyaJ2AU1ncFEymbDSa4NoWw4mT64byQsS0SaouUJTTuIouc7uw8M6F6FQ6AJo1awZARcUx+vc/T0npTmy2OjRsMJOAgL5Ikoxt27Zd0m98fDxWq/XmDcTJFeNUFjcBk+n/2rvz+Cire/HjnzN7JntIQjaSEHahrBFBW1fUigvWVqsil9aFX1trtaCit9SrV3uv1uVapdcFLHJFq1YRldYFVFARhCA7soUsZN/Xmcksz/n9MZNxIMEkZCYTkvN+veY1zzzbfJ+HJ/PlnOc855QRHz8j3GEoStCcmxDN44UVfNhkBeDeST9n7pirMQc8cOpyNXDkyGOUlb+J0RjPqFFLyEi/EZ1OPZR6OlLJIsQ0zUVbW6UqWSgDytQYK1ckxZJiNnJPdgqxxsn+Ze1jthw+/Cfc7kYyM29VY0sMACpZhFhbWyWgYbGkhzsURQkavRAsnzC8w3ybrZCDBx+grn4TMTGTGTvmEaKjx4UhQiXYVLIIsfZmsypZKAOZpjkpKl5GYeFShDAxZvRDpKffoBp1DCAqWYRYfcPXAFgjssMbiKKESENDHgcOLqG19TDJybMZPWoJZrNq5jrQqGQRQm53M8eOrSAxcZYavUsZcFyuRo7k/5mystexmNOYNHEZiYkXhjssJURUsgihkpJVuN2NDM++PdyhKEpQVVV9xMFDD+By1ZM57BaGD78TgyEy3GEpIaSSRYi43a0UH/sbQ4acT0zMxHCHoyhB4XTWcPDQQ1RV/YvoqPFMnvQ3oqPHhzsspQ+oZBEipWWv4XLVMTz7t+EORVF6TUpJZeX7HDr8n7jdrYzIWURm5m1qpLpBRCWLEPB47BQVLSMh/ofExk4JdziK0ittbZUcOPgANTXriYmZxLixjxIVNTrcYSl9TCWLECgtex2Xq5bhw+8IdyiK0is1tRvYt28RmmZn5Mj7yBx2s2oOO0h1u9dZIYReCLFDCLHW9zlBCLFOCHHY9x7fyTZjhBA7A15NQoi7ghh/v+PxOCgqeoH4uBnExeWGOxxFOSWa5iY//wl27boFiyWF6WeuJSvzNpUoBrGedFF+JxA4tuF9wCdSylHAJ77Px5FSHpRSTpZSTgamATbgnVMPt/8rK38Tp7NalSqU01ZbWzU7dv4bhUXPkZp6LbnT3iYyMifcYSlh1q1kIYTIAC4HlgfMngOs9E2vBK7uYjcXAflSyqIexnhaOXbsZWJjpxEXp8baVk4/9fVfs3XblTQ17WLcuMc4Y9yj6PWWcIel9APdLVk8DdwLaAHzhkopywF878ld7ON64O8nWyiEWCCEyBNC5FVXV3czrP7H5aonOnoCwteFs6KcDqTUKCx8jm923ITBEMWZuatJS/1ZuMNS+pEuk4UQ4gqgSkq5/VS/RAhhAq4CTjqKiZTyRSllrpQyNylJjSinKH3F5Wpg1+4F5B99guTkH3Nm7jtERY0Jd1hKP9Od1lDnAFcJIWYDFiBGCLEKqBRCpEopy4UQqUDV9+zjMuAbKWVl70NWFCVYGpt2sXfvHbS1VTF69H+QkT5PlYqVTnVZspBS3i+lzJBSZuOtSvpUSnkT8B4w37fafODd79nNDXxPFZSiKH1LSg+Fhc+xfft1AEyb9gbDMv5NJQrlpHrSGupEjwIXCyEOAxf7PiOESBNC/Kt9JSGE1bd8dW8CVRQlOOz2ErZ/cyP5R58gKekSpp/5HrExk8IdltLP9eihPCnlBmCDb7oWbwunE9cpA2YHfLYBQ3oTpKIovSelpLz8bQ4dfhiAM854kpShc1RpQukW9QS3ogwCbW1VHDjwB2pqPyUubjpnjHtcdZuv9IhKFooygLV3AHjw0INomoNRo5YwLGM+QvSmBloZjFSyUJQByums5eDB/6Cq+gNiYqZwxrg/qyexlVOmkoWiDEBV1R9x4MAS3O4WRuTcQ2bmreh06s9dOXXq6lGUAcTlauTQof+konIN0VHjOWPK4+oBOyUoVLJQlAFASklt7WccOLAEp6uW4cPvJDvr12pwIiVoVLIIIperCY+nFYMhOtyhKINIU9MejuQ/Rn39ZiIjRzFx0ovERE8Id1jKAKOSRRDV1X2BlB6GJPwo3KEog4DdXkx+/pNUVq3FaExg9Kg/kp5+AzqdOdyhKQOQShY+L7/8Mi+++CL5+fm0tLSgaRoxMTHY7XZiY2PRNA1N06ioqCA6Ohq73YbBoMPl8qBpGiCwWvXY7W70+rNxu91IKTGbzbjdbjweD1arlezsbG655RYWLlwY7kNWTlNOZy0FhX+ltPQ1hNCTnfUbsrIWqBKtElKqsTVQX1/PM888w4oVK2hqaiI5ORlN06iqquLll19Gr9ej1+u56KIpjB49DLNZR0KCwGzxsGRJIjodJCToMBo1dDqB0WikpqaGbdu2IYQgI8P78NNLL71EfHw8zz//PK2trWE+auV009ZWxZEjj/HV5gspKXmF1NRrOHvmp4wYsUglCiXkhJQy3DF0kJubK/Py8kL6Ha2t+RzY9SDOkkb21SSydVcj1VVOPvroE+x2OyaTCafTCYDBYEDT3EgJJ54uvV6Px+Pp9DsiIiLQNA29Xo/NZgMgKiqKnJwctm3bhslkCukxKgODzVZIUfEyystXI6Wb5OTLyBn+OyIjR4Y7NKUfEUJsl1KGbCznQVkNpWkuduX9GkdbCSZTMkdLvqapsY1PP20iOTmCY8fwJwoAt9uNXg833HApmzbtp7Dw2En3LYSgPQHb7XZ++9vfsnTpUqKiomhpaaGlpYXx48erRKF0qal5L0VFL1BV9SE6nYG0tJ+ROexWrNascIemDEKDMlkUF/wNuyef9D13Elk9mWOl/8u+fVtIivVQVGZD0zpu4/HAunU7qKqqOmH+8aWKE0tqS5cuBaClpcWfSL744gtKS0tJT08P7oEppz2Px05V1QeUlb1JQ+M29PoosrIWMCzjF5jNalAwJXwGXbLweOwUHF1KZN1Ecn7+SxrqGkh7NYu1az8iOz4Bt6sFEEDH6rlzzz2XTZs2UV5e3mGZwWDA7XZ/73e3JxKbzUZLS0swDkcZIJqb91Fa9iaVle/idjcTEZHNyBGLSU+/Qd2PUPqFQZcsvj3yNzS9jYSCKzFcG4nDXUVERARjx47lm+3tI8d2fh/nrbfeOul+u0oUgaZMmcKYMeqp2sHO42mjqmotJaWv0tS0C53OTHLSZaSlXUdc3HTVdbjSrwyqZKFpbkpLXsBZMwFrwyjK/n0TLuGkaU85xcXFnf7g63Q6X9NYsFqtOJ1O/3qBywJviAcKvIfRLjs7O8hHppxO7PZjlJS+Snn5W7hc9VitIxg96gFSUq7GaIwNd3iK0qlBlSw8nmYMopXPnRNwx+k4s0HjsLuIlTte483MS1nQ8ClHbfXHbaMF3MCw2+3H/fAHLtPr9Z1+Z2etzV566SXmzZvHeeed19tDUk4TUmrU1m6kpPRVams3IISOxMSLyUifS3z8TFWKUPq9QZUs7PYSAIQxjZ/cdw7z//okm/OfR4w3899z9qO96IbdEB2tp7m58+aw7X/UJyaBk/2xn1iy0Ov1ZGZmMnHixGAckhJEHo+Dysq1lFesxmJJJTXlp8THz+jV2A8eTxsVFaspKl6G3V6EyZRIdvbtpKddj8WSGsToFSW0BlWyaP+jN+m97y//ZiGTbttNdtsB1lcf4MzqVgqh00RhNBpxuVwn3bfNZvPf5A5MECaTiba2Nt/3C4QQtLa2EhcXF9RjU3qnuflb9uz9DXZ7MVZrDi0t+6moWENk5CimTX0dozGuR/tzu5spLX2N4mMrcDqriYmeSM74p0lOuhSdTjWbVk4/g+oJboPBWx/sdDUA3h/vR+Zci81mpN4uKWzQ2lcM2MY7HRkZ6f+xj47+rnWK0WhECIHJZPJXSyUnJ/uXR0ZG+qellLjdbnQ6HQ0NDaE4ROUUlJW/Rd72n6J52pg8aQUzzvqYH56zhTPG/Rmb7Sj5R5/s9r7anDUcyX+CTV/9iCP5fyYqcgxTJr9Cbu5qUoZeqRKFctoaVCUL8P6YG/XfVRlVu800Vxxi0zE38RaosQMBN7rb+4hyOp3+0kJzc7N/eXtpI/Dmdl1dXafTZrOZSy65hJiYGA4fPsz06dODe3hKj7S2HuFI/uPU1KwnPn4mE8Y/jcmUCIBebyE19ac0N+/nWMlK0lKvJSbm+KpDl6sJh+MYjrYKnG3VNDfvpbxiNZrmJDn5MrIyFxAT84NwHJqiBN2gShb1TYcBSI7/rtmqu+EVfn69ZMGrTsp997atJgs2p8O/js1mQ9M0IiMjGTVqFMXFxdTV1flbQ5lMJiZPnszWrVu9+/Qlm4iICKxWK7W1tYC3JPP+++8zYsQInnyy+/9bVYLL4SijoPCvlJW9iV5vZUTOPWRl3YYQHRsp5OTcRWXVP9n/7WIS4s/G7ijB4SjF4SjB7W4+bl0hTKSm/oSszNuwWof31eEoSp8YVMnC5mgCIDoiAYCSI8WMzNrEyKwYams8rH6nCZ3bTJPN7t+mvW+nIUOGEB0dzf79+/2lifbmshs3bmTGjBn+m9xxcXHY7XYcDgd2ux2dTud/RUREsGjRIoYOHdrHR684HGUUFDxLecVqQJCRMY/h2bdjMg056TYGQzSjR/2Rvft+h8NRgsWSjsWSQVxcLhZLBhGWDMyWVMymJEymRFXNpAxY3U4WwvvfrjygVEp5hRAiAXgDyAYKgeuklPWdbBcHLAcm4H3a7WYp5eZeR34K9Hh7erW7vf395z33fwzfY2DTxWfwWU0sTbbXGRplRm93oPmqnLKzszGZTNTX15OWlsbtt9/O3XffjU733e2eiIgI5s+f7//c3NzAjBljycsrwOFw+Ls393g8LFu2jFtuuaXbMbd3nS6E4Nlnn2Xq1Kn+ZU899RTvvfceAEVFRVxzzTU8+eSTbN68mUWLFmEwGLjyyiu55557Tv2kDQBOZw2Fhc9RUvoaAGlp15OVuYCIiO51tzJ06OUkJl6AThehmrgqg5eUslsvYCHwGrDW9/nPwH2+6fuAx06y3UrgVt+0CYjr6rumTZsmQ6G2botc/0mOnPfMv8v39xbLLROnyf1jxsrhs26TxoQMid4khdBJvEnN/0pLS5MJCQkyLi5OJicnd1huMBhkVFTUcfOE8M7/7rOQgCwuLu52vEuXLpUmk0kaDAap0+mkTqeTq1atklJK+eSTT8qcnJwO3zFmzBhpMBhkZGSkNBqNEpBms1lOnz5dxsTE+PdjtVqlEELW1dWF5Fz3B05nozxy5An52YYJcv0nI+W+/YulzVYS7rAUJSSAPNnN3/NTeXWrNZQQIgO4HG8Jod0cXyJoTwhXd7JdDHAu8JIvMTmllA3d+c5QiI+bTp0zhwuGfcGiFXloOh2NHg8tee8x9Mb/xhCbTOB/HC0WCwAVFRU89NBDLF68mJEjR2IwGPytnBITE4mJiTmuWW18vCAmRjB37lzi4+OB757L6G71U319PU888YS/OW576eTWW28FYOHChdx+++3ExMT4t5FScvToUYYNG4bFYvHfO3G73fziF79g8uTJ6PV6NE3DZrMxbtw4f3wDhZQadXWb2Ld/EV9uOpvCov9lyJALmHHWR5wx7tFulyYURTled6uhngbuBQJ7NBsqpSwHkFKWCyGSO9kuB6gGVgghJgHbgTullB1G/hFCLAAWAGRmZnb7AHpCCAERmURpO2kzmFg+/27OefX3jEuyUXnma8Qarqb2L3/1r9/eFFZKyT/+8Q+OHTtGfb23pq198KKamprjuv0AqK/3JoY33ngDh+O7G+UADz/8MA8//PBJY3z55Ze59957qampOe5hPrPZTFtbGw6HA51O51/W3py3/bPL5aKgoOC4fXo8Hu666y6cTiepqan+jhDbB2UaCGy2AsrL36a8Yg1tbeXo9VGkpFxFRvpNREefEe7wFOW012XJQghxBVAlpdze1bqdMABTgeeklFOAVrxVVh1IKV+UUuZKKXOTkkLTFbPb3UqUZwt72rzNGddXRfLksNnMNDu5XhgxXHULmLz3M/R6vf/HVK/Xs2TJEqxWK9HR0UyZMsW/z5iYGEaMGAF0fIo7MTG2w/zly5efdJS85ORkfvnLX1JdXd3hCfH2B/vg+KfH24uIXWlv2hvYY+7evXv9ye905HI1UVL6GtvyfsbmLbMoLHqBqMhRjB//ND/64deMG/tfKlEoSpB0pxrqHOAqIUQh8DpwoRBiFVAphEgF8L1XdbJtCVAipfza9/ktvMkjLA6VrcWEg42WWRBlQGjQmjWK1dEz+WH6XiY2fQZO74+y0WiksrLSv+2WLVtYs2YNEyZM4Mwzz/TP93g87N69m5SUFP88g0GP2QzDh3urnAJvhhsMBoxGY4fY/vnPf1JdXX3S2M1m86kf+EmkpaVx+PDhoO83lDTNTU3NZ+zZewdfbjqLgwf/iMfTwsgRi/nhOV8yefIKUoZeiV5vCXeoijKgdFkNJaW8H7gfQAhxPnC3lPImIcTjwHzgUd/7u51sWyGEOCaEGCOlPAhcBOwPXvg9k1/6Dk2k8MOc8/hgVhq3r9nEP01n0fKvp/G4LUzZ/N98bhK4XAKPx+MvEQghcDqd5OXlMXLkSP9YFHq9nrvuuguLxUJOTg6VlRUAaJpE08DhaEGv16PT6fyDJN14442djpK3ZMmS7409sGQRLJWVlWRlZaFpbl91VuedIfYHLS0HKS9/m4rK93A6qzEa40lLu57UlGuIjp6gWikpSoj15jmLR4E3hRC3AMXAtQBCiDRguZRytm+9O4BXhRAm4Cjwy158Z69YXMXkMY75GUlYjXo8rZXoEkdx/YSpLPzdF9Q1ukhJG0Z5aTlCCP8PtNFoZMWKFVitVj7//HOuvfZawFti+Pjjj/nyyy+JjnYghHeMbk3TEAKOHav2N5nNzMyksbGRtWvX8rvf/a7DKHk1NScvVYSKy9XCunW3kTGsAJ3OQnb2bxiW8Yt+88PrdNZSUfkeFeXv0NyyDyEMJA65gNTUaxgy5Hz1TIOi9KEeJQsp5QZgg2+6Fm9J4cR1yoDZAZ93AiEbRLy7pNQQ7hrqScDhuxmdGx/Nux++x8b8Upz1VnQXC5pKmzDXGmltsaNpGnFxcbjdbsxmM88//zyvvfYamzZtAvC3Uvr449WcdVaWfzhWq9VKYqKOF5ddyHXXfoaUkuLiYlJSUoiOjj5ulDyby8bTK/+Ep83eIeZQ0eu9w8R6tCZS0xpISbmGlpb9HD78CBGWdF83FiW4XA1I6fY+iBaRgdmcgl5nQae3oNdZMBoTMBgiu/7CHmprq+RowV8oL38bKd1ER09g9KgHGDr0SkymhKB/n6IoXRs0T3A7XXUIPDQQzyV5h4iv3cD4Qj221X8ndewo1u3dhfaqBANEmk1kZGRw7NgxWltbSUxMpKCggFmzZhEdHY3VamXmzJls27aNHTt2MHRoBomJnuOapVZW6vnL01/x1ltvcc011wDelkrnn3/+caPkbT3/fJwXz+a8iy7h9ddfP2n8Op0Os9mM3f5dUjEYDHg8HoxGoz9xdSU52cCFF41n69eVvP32KiZP9ub78oo1NDZ+w+49v/atKTAYYhBCh8vV+U1wIfRER08kIX4G8fEziY2d1qt7BW53M0XFyygu/htSuklPu4H09BuJihp9yvtUFCU4Bk2ysNuLABgT4WJj7Rbqh5zPJ/mbSEpKZuP6daTERFHptuFp9tDqdtLaWuxvolpXV8fUqVOpra2lsLAQgPXr1wP4epC1E9iJrF6v54orpvLOO9v44INLAPy91T766KP+9XaXNJDQ4G0ZNWbMGIZExFFrD9hRAJPJ5E8UOp0OIQQjR46kpKSkR+N5V1d7+OjDYhYsWOBPFACJQy5k1KglWMxpREWNxmIZhk7nvTzc7lYcjhLanNVoHgea5sCjObDbiqhv2EJR8YsUFj2HECZiYibgdrfQ1laJEHp0OhM6YULojL5p4wnTJnQ6E0IYqKv7EperjuTkyxmRswirNavbx6UoSmiJ7jS77Gu5ubkyLy8vqPssK3+Lb79dzCPlFlqIJO3roXz+/Ec0ulwYjEYmTpzIN99842+Gqtfp0HzNUrsay6Jd+/MO3tZPEk3z7uvcc89l165dWCwW8vPz/Q/01bc6ufeVLzhn/1dMMEzm9ZFGPhhr4pa/fsprW9YgdDr++uYyTBFm1q1b5++244EHHiApKYk77rjD/91RUVGMHTuWqKgoAB5/4mfY7St54YUj7PjGSEODjjPOmMC77757XLfpweB2t9DQmEd9/WYaG3dgNMZhNqeC9KBJF1JzoWlO37QzYNqFJp1omnc6wprFiJyFHXp3VRSla0KI7VLKkFX5D5pkcST/CY4WPsctf3cQ900CRw8eRa+DthYbHUfOPl57J4CdjdHdE7GxsVRVVXXaGkpKDy5Xg79TO7e7mdq6LzCbklmzZm+3+4e66qqLmfdvDWz+ahPLlzuJisrk6quv55577qGurs5fJQawadMmqqqqBtxT3IoyGIU6WQyaaqjy+p0U1WuYtlhISU1hn20v3U2T7V1t9FZjYyOff/45s2bN6rBMCD2a1saxkleoqfmE+votSOnC7c7imWca2LJlC6WlpcybN48vv/zSv93ChQtZuHAhALNmTWfM2A20tETw0ksRvPfeV2RlZXH55ZczZ84cRo8ezYYNGwDYunUrDzzwgEoUiqJ0y6AZKc9uL2T7/jamTJzCxo0bMejD0zx05cqV/mkpJS2thykoWMrWbVex6asfcejQg7S2HkZKb7XXuq2RyKFjMZlMDB8+nJaWlg7PXGiai6+//gOHD+8kN3csZ01/H5tNR1aWt84/NzfXnyTarVq1iptuuim0B6soyoAxoJNFRcV77P92MRWV7yNc1Wzd1Mqad9bgcrlwecJT/bZq1SqystJZuOjHbPn6Yr7++sccLfgfhDAxcsS9zDhrHUOGnA/o2NXwc17NG0lBMzjd3pJNbGzscaPvORxlbP/mBl555QWuuGImudP+QUREJomJiezatQun08n69euP28btdrN27Vquvvrqvj14RVFOWwO2Gqq+/mv27b8bKTXKy9/i/TWN7PykCekJb1xDhxrxeKq56KIjWMxnMyzjFyQmzcJi/q67kIbGfeQ3jeGZreegmbajOY4y4cGPOPTIZTQ2NpKQkIDDUUZl1T8pLHweKd189dUQ3nhjOTqdt1uQZcuWsWjRIqSU5OTkkJaW5t//xx9/zMyZM/03wxVFUboyIJOFw1HG7j2/xW6PZueOS5hQdYQ3XvsIqykCl8uD093VLe3Qqax0ERlpISH+RaZMubDTdZrt9dS2JvPUdZNoaMjk13Nfoa3NyaFD2zAaW9m9Zy5NTTsAiI3NxWhYgMn0B0aNGuXfx/jx4/nwww9xOp385Cc/4bLLLvMvW7VqFfPmzQvtgSqKMqAMuGooTXOye/evcLls7Nt7LpfMmsPujUewGRNwRrpxu7tuAhtqN944j1tvvePkK2jNtLojuHBsMnpLNKPOycXyz19wzTU/4pc32zl4oJr16yYxc8an5E57g7ff3sjcuXOP28VTTz3FBRdcwKWXXsrtt99Oe0++LS0tbN68mYsvvjiUh6goygAz4JrO1tdv4Zsdczlw4BziYi9lzs9+RvrDT2H/v5dxlR1CaN7+m8JFCEFubi5Op5PPPvusQ2skKSWffDqafxVchMd6E9GuF7lg2CbM5gzS069jaPJsrNbhYYpeUZT+SjWd7aHaOm/1TEN9KjfNvYL1hSXoomO8Y2p7wJBhxFUSvtKF2WzG5XLR0NBAXFxch+UeTytCaGTHFpNivYuEiEaGZdzCiBG/R6+P6PuAFUVRGIDJoqXlKC6XiQkTziY2NpaPdnyFcdwPMDeUYhfgrundg3Wnqn00PYfDQVlZGa+//nqnvbt6PN7uP8YPOYgwZDNt0jJiY6d0WE9RFKUvDbhkERU1FL3eRX7+EQBqa/LINE7FkB7L9kM2tDbvwOOhbhTVngjau/9oH/503Lhx7Nu376TbmUxJJKfdgcOtY+q4/4deH/xBjxRFUXpqwN3gNpuS0OkkdnsVTqcTfUMk7vr7OWtqInEREGMRnPgstsn3wx7MoX8MBgNnn302Op0Oi8WClJKoqCgaGhqYNWvWSYdWFULHD8bexZkTfqcShaIo/caAK1mYzMned5Odmpoa5PbNuD4/xJt1buocuk677XD67ngHs7QxduxY6uvryczMpK2tDafTSXZ2NsuXL2f69OlB/CZFUZTQG3gli4BksXLlStasWENtrZvaWg9SCITB+7/1rMiOh64TnZ8One674VUDBX7W6/W+db2dDn7wwQeUlpYSHx+PEIJhw4Zx5513qkShKMppacCVLMwmb7JIS9/IbbeuQKfzUFkJOh1YLGaklDgxUG7veKM7KSaDqsZjyA5dDApAEtjMOCIiwl9ayM/PZ9KkSdx88838/ve/JzY2lsTERBoCB7lQFEU5jQ24koXJlyz27GmkrQ30eh1nTIhA08Bus9HmcCBNJhKsHbetbvImivPOyyEry+if3151FRMT4xurAnJycoiJicHlcjF16lSOHDnCQw89hNvtZvHixZjN6n6DoigDx4BLFjU1GwE4eKANgx7aHHD0iMO/XAiBtNtpdHTc1mT2jjOxd28RlZXfzW8fLKipqcmfOPbt24fRaEQIwbx582hsbOSpp54iJSWFu+++O0RHpyiKEh4DqhrK43Gwc/s9uFrN1BZEYLc1odODw+Eb/U6v91YlSYld0wEaibER1DR6hyt1Or1df9fVeZABPQ4mJydTUFDgHwmvXW1tLVJKvvzyS959910OHTrEww8/3HcHrCiK0kcGVMmitm4jBksLpZuTkc3e0oDmkfjuT+PxeNA0DXNMLJarrkVnNFLbFFjq8L5LCRaLxT+/oqICg8FAcnLycTe1pZT88Y9/5K233uL999/HZDIxf/780B+ooihKHxtQyeJQwbu42qKw18wgIdKKTgg0CSbzdz/wUVFRGI1G2L+bCeNGYzIJf5KIj0/EYDCg0+lwOBz+Fk56vZ5Zs2Yxbtw477ZAfHw8s2fP5sEHH/Tvt6CgAINhQBXWFEVRgB4kCyGEXgixQwix1vc5QQixTghx2Pfe6ficQohCIcQeIcROIURwB9Y+ga1lN47asfws7XJmxE9nSJQVo16P0ykRAhISYtDrdVj1OuZfOJbi4gO43RqRkREYjUZeeuklf7ccERER/tLF3Llzqays5Ntvv/Xfs4iKimLOnDmhPBxFUZR+oyclizuBbwM+3wd8IqUcBXzi+3wyF0gpJ4eyR0QAvWYnw+Md0OfS8dcgXG5mDk8iM9NEaqoBm82Gpmk8//x1jBmzheTkSD7/fC3jx09ESsmvf/1roqKiWLFiBeeeey6tra0IIfjss8+Iiopi8eLF5OTkoNfrMRqNPPvss2zdujWUh6QoitIvdKvORAiRAVwO/AlY6Js9BzjfN70S2AAsDm54PaPTuxHSW3UUZ4nm1qRInjhaSXySHqPFihBOZs9Ooar6Hd54w0xpaRMXXvhTNE3D4/EQGxvLp59+yq9+9SvKy8sZPnw4RUVFxMXFUVlZyerVq6mrq+ORRx7B4XAwcuRI9ZCdoiiDQncr2J8G7gWiA+YNlVKWA0gpy4UQySfZVgIfCyEk8IKU8sXOVhJCLAAWAGRmZnYzrONFRo2gOLmIr1p0FMZ8y3n3nsMfC438Y30ebk8j9y4egskoKS6exZbNLx+37ciRI9m/fz8AV155Je+//z4AS5cuZfbs2YB34KAf/OAH3H333erehKIog0qXgx8JIa4AZkspfyOEOB+4W0p5hRCiQUoZF7BevZSyw30LIUSalLLMl0zWAXdIKT//vu881cGPjh79C0cLn+VPuv/icstBZrCZNttBAGJjpzE0eTbJybP9XYIoiqIMFP1h8KNzgKuEELMBCxAjhFgFVAohUn2lilSgqrONpZRlvvcqIcQ7wHTge5PFqRoy5DwKCp9hiXY/2MASO43MUUtITvoxFktqKL5SURRlUOgyWUgp7wfuBwgoWdwkhHgcmA886nt/98RthRCRgE5K2eybvgT4z6BFf4KYmIlkZf0KkylRJQhFUZQg6k3F+6PAm0KIW4Bi4FrwVjsBy6WUs4GhwDu+B9kMwGtSyg97F/LJCaFj5Ih7QrV7RVGUQatHyUJKuQFvqyeklLXARZ2sUwbM9k0fBSb1NkhFURQlvAbUE9yKoihKaKhkoSiKonRJJQtFURSlSypZKIqiKF1SyUJRFEXpkkoWiqIoSpdUslAURVG61GXfUOEghKgGinqxi0SgJkjhhEJ/jq8/xwb9O77+HBv07/j6c2xwesQXKaVMCtUX9Mtk0VtCiLxQj53RG/05vv4cG/Tv+PpzbNC/4+vPsYGKD1Q1lKIoitINKlkoiqIoXRqoyaLTAZb6kf4cX3+ODfp3fP05Nujf8fXn2EDFNzDvWSiKoijBNVBLFoqiKEoQqWShKIqidOm0ShZCiDeEEDt9r0IhxM4TlmcKIVqEEHefZPvHhRAHhBC7hRDvCCHifPOzhRD2gH0/H4bYEoQQ64QQh33v8QHL7hdCHBFCHBRCXNrT2L4vPiHE9ID5u4QQP+nh9iE7dz2I7UEhRGnAurMDlvWHc9fn110PYgvXdXexEGK7EGKP7/3CHm7f63MXpPhCdu0FIbbgXndSytPyBTwJPHDCvLeBf+Ad+rWzbS4BDL7px4DHfNPZwN4wx/Zn4D7f9H0BsZ0B7ALMwHAgH9AHKz7AGnBO2sdSN/Rg+5Cdu+7GBjzY2XntL+cuHNddD2IL13U3BUjzTU8ASnu4fVDP3anG11fX3inGFtTr7rQqWbQTQgjgOuDvAfOuBo4C+062nZTyYyml2/dxC5DRX2ID5gArfdMrgasD5r8upWyTUhYAR4DpwYpPSmkLOCcW4HtbPHR2fMHS29g60S/OXTiuux6cu3Bddzukd1RN8P5dWIQQ5u5uH2y9ja8TQTt/pxpbsK+70zJZAD8CKqWUhwGEEJHAYuChHuzjZuCDgM/DhRA7hBAbhRA/CkNsQ6WU5QC+92Tf/HTgWMB6Jb55QYnPF+NZQoh9wB7gVwEXWLe2J0Tnroex/dZX3P5bQFVKfzt30EfXXQ9iC9t1F+CnwA4pZVsPtw/WuettfKG+9np77iAI112PxuDuC0KI9UBKJ4v+IKV81zd9A8f/D+Mh4H+klC3eJNzld/wBcAOv+maVA5lSylohxDRgjRBivJSyqa9j6yzcTuZ1+r/EU4wPKeXXwHghxDhgpRDiAyml4yTxnLh9KM9dd2N7DngY73l5GG+R/Wb62bnr4+uup/+uHcLtZF5Qz51v2/F4q0gu6SKeU7ru+iC+Xl17fXHuTvW66xh9EOv8+uKFN8FVAhkB874ACn2vBqAO+O1Jtp8PbAas3/MdG4DcvowNOAik+qZTgYO+6fuB+wPW+wiYGaxz18k6n53s2Lu5fdDOXU9iC1gnG199bD87d3163XU3tnBed3irRQ4B5wTh+E7p3AUjvlBee0E4d0G77np8YsP9An4MbPye5Q9y8pvIPwb2A0knzE/Cd/MJyAFKgYQ+ju1xjr/R+Gff9HiOv1F2lFO8UdZZfL59tt8EywLKgMQebB+yc9fd2PD92Pmmf4+3rrjfnLtwXHc9iC1c112cb/8/PcXtg3LuehtfqK+9XsYW1Ouuxyc23C/gZbz1rydb/iABP8jAcnxZE+9NpmPATt/red/8n+K9UbQL+Aa4MgyxDQE+AQ773hMC1vsD3tYUB4HLgnnugHm+Y9/pO/arO4vve7YP2bnrbmzAK3jr5XcD753wBxz2cxeO664HsYXrulsCtAack51Acl9fd72NL9TXXi9jC+p1p7r7UBRFUbp0uraGUhRFUfqQShaKoihKl1SyUBRFUbqkkoWiKIrSJZUsFEVRlC6pZKEoiqJ0SSULRVEUpUv/H3wWyr9JLTLHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        plotPoly(vtdGeom[p])\n",
    "\n",
    "plotPoly(wholeMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1abn5KQBOeE/wesrrDGo1iN5NepfbeSNNka6YEKIJcAUwPrhNSrlGSrm2kEN7AhuklJukFXrzFXBVSY0906gWVY0pN07hlk638K+//sVTfz5VKQVj17Fd7D2xl+6NyqzgpY1NiVizfw0Xf3Ix+hadT676hBcGlL+/4P1F77M/fX+VnlVA0X0WScDTQM1ijt8Y2B72egfQK78dhRAjgBEATZvaVUyDRClRfHb1Z9SNqcsbxhvM2zGPdrHteHXgqwU65wzjwEk+ibJk65GtALQ6q1WZn8vGpihkZGfw8uyXGTt3LDVcNfjt1t+IaxlX7nakZ6fzWsprXNryUi4696JyP38kKVQshBBDgX1SysVCCK2Y4+cn4fn+NJZSjgPGgdXPopjnOa1xCAfvDH6HJrWa8KznWeZun8uNHW9kUKtBJ+1r5VHMCCssWLIIp+Kw+e+jACxeuYHBbcr0VDY2pyQtPY3vVn/HaymvsfHQRm6/4HZeH/R6RLOzi8MHiz5g34l9VX5WAUVbhuoDDBNCbMFaRrpECPF5EcffAZwb9roJsKtYFtoAIITgmYufYcqNUwAKnFUE8yiswoJmsZPsiothbOKeG34AU/DCh8kYRvFKhtjYlAZTmqxPW89nyz5j2JfDaPhGQ+6fdj/Voqox/fbpJF+dXGFCkZ6dzti5Y7mkxSVc3PTiCrEhkhQ6s5BSjgRGAgRmFk9KKYuaWrwQaCOEaAHsBG4C8q+IZ1Mk9p/YD8DZ1c7O9/1gHoXXa5Y4ya446Pp6vCcAbxR+Vwa6vh5VbVmm57Q5c/D6vew6tosdR3ew8+hOdhzdYT0/Zj1ftX8VR7OsmW2jmo14tNej3NrpVjo36FzhuQyTlk9i74m9/F/3/6tQOyJFifMshBBXA/8DzgamCSGWSikvE0I0AsZLKYdIKX1CiIeB3wEFmCClXBURy89QDqRbtZsKmllYeRQDQj4LyOSBB34FJPHxF6CqTSJqj6a1wVVNkBnlw4GCptnrUDalx9hu8OKsF/l9w+/IPCvX1aOq06RWExrXaswtHW+he6PudGvUjU71O0Wsamwk6N6oO3Vi6vDor4/S8qyWobYDVRW7B3cVI+G3BMYvGc/xfx0vdF/D2MGAAZPIyvID4HI50PXbIi4YhrGJh6ffzwo5kxUPLqddvXYRHf9MxW/6WZu2FpfionXd1hVtTrkwZ9scXpj5AtM3TadetXrc3eVu2sa2pXHNxiGBqB1du8JnDUVl1b5VDPliCAfSD/DFNV9w1XllFwxq9+C2ycWaA2uKnPym69vwev2BVxKvV5KYOJfExItR1cj1F1bVlkzrlMz5757PiJ9HMGP4jHKNYz8dyPZns3r/apbsXsKS3UtYvHsxy/YuIz07HYDz6p3HefXOo2uDrnRt2JVujbrRoEaDCrY6N3uP7+XndT/z15a/iHXH0uHsDnSs35EO9TtQJ6ZOgcf5TT8zt87kpVkvMWPLDOpXr89rA1/jge4PUN1Vvfw+QBnQoX4H5t8zn2FfDuPqr6/m9UGv83jvx6uM2IVjzyyqGI3fbMylLS9l4j8mFrpv7pmFABQcDkF0tBOP54aICgbAx0s+5p6f7uHDoR8yotuIiI59uvLnxj957q/nWL53OVn+LABqumrSpWEXujawROFA+gE8mz2sT1vPurR1oWWZhjUa0q1RN7o17Bb6AeEzffhMH37TH3ruM334Zc7rLF8W+07sY++JvdSOqU3rs1rTuq71OK/eedSMLlqEfHp2OuvT1jNt/TR+WvcT83fMRyJpUKMBx73HOe7Nmf02qtkoRzzO7kCTWk1YvHsxs7fNZu62uRzzHqNBjQY8fdHT3Nf9vpM6QlZ10rPTiZ8Sz+Q1k7mu/XV8ctUn1HDViOg5ynpmYYtFFeJgxkFix8Yy9tKxPNXnqSIdYxg7SE5ewZIl+1i0aB+mCYoiGDXqYkaOjGw2qZSSS5IvIXV3KmseWkPDmg0jOv7pyFN/PMXrxuv8s/c/Q2vvreu2LnBmdizrGEv3LGXx7sWhGcjfB/7GlGaRzykQ1KtWj/rV63M48zA7j+3M9X6z2s3oWL8jnep3ol29dpzwnmDnsZ3sOrYr5+/RnRzJOhI6pnuj7gxrO4wr213JhedciESy/ch2Vu5byar9q6zHvlWs3r+aDF9G6LgOZ3egb9O+9GvWj3+c9w/cUe5iXsGqgylNbvv+Nr5c+SV/3v4nl7a8NKLj28tQNiFW7F0BQKdzOhX5GFVtgqo2wTB2Ehf3TSj/QtPOLfzgAIaxDl1fhaZ1QFULXgITQjBu6Dg6vd+JR359hO9u+K7I5zhTCfoiHuv9GE1rF56MWjO6Jn2b9aVvs76hbSe8J9h2ZBuKQ8HpcKII62/wEdwefE9xKLnEKD07nc2HNrP+4HpW71/Nyn0rWblvJX9s/INsMxsARSg0rNmQxjUb0y62HZc0v4TGtRpzbq1zGdBiAI1qNsplp0DQrE4zmtVpxhVtrwht95t+thzewrYj2+h0Tqdyrfpa0TiEg3/1/RdfrvySvw/8HRILKWXVWJaSUla6R7du3aTNyfxv/v8kichnXl4tU1Iyi318SsoOOXq0IVNSdhTjmLXS7b5VKsqN0u2+VaakrC30mNGzRksSkVPWTCm2jZHCNE259/heeSjjkMzIzpCmaVaYLafCs8kjSURO3zi9XM+bkmLI0aNflSkpRoH7eH1eue7AOrn72G7pN/3laN2pmZ+SIt8cPVrOT0mpaFNKROcPOsvOH3SWpmnKp/54SpKIXHug8P9XhQEskmV4X7ZnFlWI6SuWQkZtXhvl5h3XXjyec1DV6CIfr6qNi+2n0PVVeL0+/H4Tr9eHrq865ewC4MmLnuSrVV/x0C8PMaD5gELbwpYFL816if/o/8m1LVqJpv3Z7fnj9j8qzS/aoK9h/cH1J5WjMIwF6PocNO1iVLVkZa0NYx66PhtN64uq9g5ti4u7HK/Xi8vlwuP5LfReOFFKFG1iK1co9ALD4Oq4uJDteftbVwVGdB3Bg788SL9P+zFn2xwAlu5ZWumrNtshK1WI1J0rYV87TL/A65XoeiYAhnGcMWN2YxiFh9MWB8PYxLZtx3A6FRTFgcvlRNM6FHpclBLFR1d+xO5juxnpGRlRm4rLSwNeYkzcGP7b/7883PNhVu9fza3f34rf9Bd+cDnQqGYj3E4369PW59puGAuIi/sH//73aOLi/oFhLCj22JYoDOHf/36BuLghGMY8AHR9Fl6vF7/fj9frRdcLLBxd6Zir67lsn6vrFW1Ssbntgtuo4arBvB3zePmSl2lcszEvzHyBbH92RZt2SuyZRRVBSkmasgYl7SpQwOUSaFoMhnGcuLi1eL0Sl0vg8bRDVUsfZWEYm4iLS8Lr9aEoNbn33h7Ex/ctdFYRpGfjnjzW6zGS5idxa6dbI95oJj07nWglusAkrPPqnQdYXfw6N+gc2t42ti33/XwfL89+mf/0/0++x5YnDuGgdd3WrDu4Ltd2XZ+T54Y+p9izC12fnWeM2ahqbzStHy6XK/TrXNP6RfIjlSl9NC2X7X00raJNKjY1o2vy262/Ud1Vnc4NOtOmbhtu+O4Gpv49les7XF/R5hWIPbOoImw7so0TvqP889aejBpVJ7QEpevH8HploBaUJDl5L2PGbMEwjmAY+xkzZhWGsb/Y59P1dYHlJ4nfr9C0aeMiC0WQUZeMolntZtz7071k+bKKbUN+ZPuzGTVzFLVfqc15757Hx0s+xuv3nrRfMDFw7YHcVfTv7Xovt19wO4l6IrO3zo6ITaWlTWybk2YWmnYxLpcLRVECN/Ti1xbStL55xrCc4qraG4/nN0aNSixwCaqy0lNVmeLx8K9Ro6rkElSQPk37hH7ENKllJclGKVEVaFHh2DOLKsLKfSsBuKp3F/rckOMD0LSauFyCrCwJ+Pj44634/RIhwOE4hGl6A9VnL0FV868nlR+a1haXyxkWPVX89dQarhq8f8X7DPliCG/Ne4tnLy5do8R1aeu49ftbWbRrEVefdzVbDm/hnp/uIXFmIk+qT3JP13tCSVxt6rZBIPj7wN+5xhBC8P4V7zNz60we++0xFo1YVOEJhG3qtuGntT/hM304HdZ/SVXticcztVQ+C0sUfjnJZxF8ryqJRDg9VbXKikR+zNthLQ9W9sZI9syiirA2zfqF/NvnzTCMnLVNVa1BUlITHI5sTDOT7GyJaYLfL8nOdpa4+qyqtsTjSWDUqCvxeBJKXBxwcJvBtKjTgmV7l5Xo+HBu+/42Nh3axDfXfcP3N37P4hGL+fXWX2lRpwUJvyfQ/O3mvDzrZQ5nHsYd5aZZnWah6xZOdVd1Xol7hdQ9qXyS+kmp7SotbWPbkm1ms+3ItlzbVbUnI0f+s8TObWuM3owc+VSVFYayYIFh8NaYMSwwjIo2BYDDmYcB2HhwY8UaUgi2WFQRZq/+G9LrMuY/0cTFHc4lGGlpWUiZTU5+ZfBJNkJQ4uqzqtqSkSMvL3UV2SNZR4h1x5ZujMwjLNq1iEd7Phpa1xVCcHnry5l15yxm3TGLHo168PyM52me1DwUXbL+4Pp8x7up4030bdqXJ/98kl3HKrZqfpu6VsRR3qUom8gTjKYa/e9/c3VcXKUQjAd7PEibum244osrSN2dWtHmFIgtFlWE5TvXQlpL/H7IygJdzxELTauDy+VAURxERTlRFB8ORzoul5P77mtT4BKUYexizJgFGEbZ3SxNaXIo4xB13XVLNc7c7XORSPo1y98Z27dZX3659RcWj1hMtDOaET+NoGmtpmw5vCXf/YUQfDzsYzJ9mdz/8/0V2q42GJ66Lm1dIXvalJbKGE11To1zmB4/HXeUm+FTh1e0OQVi+yyqCAfFBkizHJSmCbGxORmfqloLj+cCdP0wmlYHyEbXD6Bp9VDV/H/RG8Yu4uK+C+uodx2q2ijffUvD4czDSGSpZxazts4iyhFFryb5duUN0bVhV94c9Ca3TbmN/en7OZB+gOPe4/nW4WkT24aXBrzEk38+yYifRpDQO4EO9QsPDY40bqebKEdUgbMgm8hRWaOpGtZoSLY/m/Znt69oUwrEFosqwHHvcQ6buxAHWyEBhwNSU7MYM+YYmlYdVa2OqtZCVWuFjilIJILo+o5ARz2J1+tH13eUiVikpacBlHpmMWvrLHo07lGkAnO3dLqFl2a/FHJubz28tUARSOidwPqD6/l06aeMTx1Ph7M7UDumNjHOGKKVaKKd0UQr0aHXPRr34K4ud0XEKS6l5Ps13/Pob4/iM310ql/0Mi42JSMYTTVX1+mjaZXGUZ68LJm0jDRuu6CofeXKH1ssqgAbDm4AQDncCr8ARYEJE/bg93sDuRUtUdXilXLWtCaBjnrBaKfI9rgIcjDjIACx1Uo+s1i8azGLdi3in+o/i7S/EII6MXWoFV2Lo1lH2XJ4S4FioTgUPhj6AS9d8hKfLv2UmVtnkunLJNOXybGsY2T5s8j0ZZLly+JE9gnGLRnHhNQJjB82vlS/Arcf2c5DvzzET+t+4sJzLmTqjVPp0bhHicezKTrlEU210DBCgtTjFOfy+r0M/Gwgs7bOonuj7lzR5ooC961obLGoAoTWstOaAyamKZHSxDQJZHKfKLZYqGojPJ7r0PUdaFqTMplVAKRlWDOLkixDrdm/hn/P+DeT10ymrrsut3a6tcjHOh1Ozq11Lqv2r2Lz4c2F7l+vWj2evOhJnrzoyQL3kVLy+fLPSfg9gc4fdOa5vs/x7MXPEu0seskVv+nnfwv+x/N/PY9E8trA10jonRAKmbUpHQvCbtIVNWtYaBhcExdHttdLlMvF9x5PgYIxb8c8Zm2dxX3d7uMF7YVKXVDQ/oZWAVbvX41AYB5oHijqBQ6HCyF8gUxuSygM4xi6fhRNq4WqFt6TQFUblZlIBCnpMtTUv6dy03c3EaVE8d/+/+Xx3o8Xq8aUIhTquusS44yJmONYCMHtF97OZa0v4/HfHydxZiLfrP6G8VeORz238BtT6u5U7v3pXhbvXszg1oN574r3aF6neURss6k8daPm6jrZASc6ASd6QWLx9cqvEQhevuTlUs2+ywNbLKoAqXtSaVa9HXsd1fAq4HJBUlJ90tIyQz4LwzhGXNwavF4TRTG56666xMc3QFXrVKjtJVmG+nTpp9z94930aNSDH2/+kfrVix/2qzgUZm6eCUDH+h2LffypqF+9PpOumcRtnW7j/mn302dCHx7q8RCj40bn2zjouPc4/53xX5LmJ3F2tbP56tqvuKHDDZX6V2RVJL9Ip4oQiz6aRpTLBYGZRUFO9LUH1vL+ove5r9t9lV4owBaLKsGS3Uvo37I/D3nqkpx8AvDTqVNULoe2rh/F6zXx+/34/Rl8+GE6n3yykzvvbEx8fEOsCKm9aNo5qGr5VVxNy0hDIKgdffKs4FjWMd5b+B5frPyC//b/L9ecfw1vGm/yxB9PMLDlQL6/8ftQFJNhrETXU9G0Lqhq4Tf/5XuXA3B3l7u5t+u9kf1QAQa3GcyqB1fx/F/P8878d5i6dio/3/wzFza4MLTPtHXTePCXB9l2ZBsjuo7glUtf4Sz3WWViT1VigWEwW9fpG8HlosoS6dRDVfk+zIle0Kzi6elPI4TgkV6PlLOFJaQs65+X9GH3s8hh7/G9kkTkGylvyJSUdOl2b5AOx2rpdK6QH364P7RfSspR6XbPl0LMlPBn6CHEdBkdPUO6XJOlonwh3e6v5YcfrpCjR8+RKSnby9z+h6Y9JOu+WjfXtkMZh+SL+ouy7qt1JYnIc147R4pEIc8ee7YkEXn9N9fLzOycfh0pKSuk2x0nFaWfdLvjZErKikLP+8rsV+Stk2+VXp834p8pP+Ztnycbv9FYNnurmdx5dKfcdXSXvP6b6yWJyPbvtpezt84uFzuqAvNTUuQ5brc8S1HkOW53RPtSVJVeFyv2rpAkIl+a+VLExsTuZ3FmE8zo7NqwK/qXGWRleTHNY5gmPPzwZjp1ikZVa6KqNfF4zic5eQ+ffLIdr9dESpDSKmcOTqTMIivrBA8//BOmaQbyK25DVa1IKMPYia5vR9POjVh/7rSMtFz+iknLJ/HQLw9xJOsIV7a9kuf7PU/H+h3p+mFX1qat5b5u9/HukHdzVZPV9VS83uxAT41sdD210NnFMxc/ExH7i0qvJr2YcuMU+n3aj9bvtCZKiSLLl8VLA17iqT5P4VJc5WpPZWZ2nuWi2RFcLqoqdaOCEY6D2wyuYEuKji0WlZwlu5cA0LlBZ6K1aBwOP2ag3bLfL9H1YyFntvVXsmdPFuvWHWft2mOYZjpOp+UM9/sFQmTh91sRVVZ+xdZ82656PDdERDDS0tOIdcfiM3088+czvDnvTfo27cvbl79Nl4ZdQvv9ePOPpGxPYfiFw09ay9e0LrhcUXi92bhcUWhal7ynqRT0aNyD5fcvZ8ycMRz3HuflS16OePOgooZkVmb65lku6ltJEuPKk+CPh//M+A9Tb5paJaLhimyhEEIBFgE7pZRDhRB1ga+B5sAW4AYp5aF8jnsMuBcQwEdSyqTSm33msGTPElqd1Yo6MXVQVXj33QY8/PBm/H5JdLQDTbOEwjCOk5y8n48/3kF2tgS8wMFA9dkM3nmnK2lpmcTGShISfiYrSyBENLGxlt9D17fnSdLbHhGxOJhxkCgliss/vxzPZg+P9HyENwa9cVI55raxbQvsFKaqHfF4korls6go2sS2YcJVE8pk7IWBaJ9gSOaUU4RkVmZ6qio/ejwR91lUJdrFWiX0p62fxrq0dZU6cztIceTsMWANEPSqPgt4pJSvCCGeDbzONfcXQnTEEoqeWHev34QQ06SUdl2DIrJk9xK6NewWej1iRD06dbL6WGiatfw0btwBHn54Oz5fVliNI6vHg5Tg80nS0rIYOTKYmCZ5+OH5+P2ShISFdOpUH007N0+S3rkRsT8tI40th7cQrUTzyVWfcEfnO0o0jqp2rNQiUR4UJySzslNVlovKgp1Hd3L111fjdDiZMGxClRAKKKJYCCGaAFcALwPBNNqrAC3wfCKgk0csgPOBeVLK9MA4M4GrgbGlMfpM4XDmYTYd2sQ9Xe7JtT247KTrR1mxIpOHHtqBzyeB8K5x1jTX4Ti56mxamrWUZSX1mej6bkaO7IzHc0PEfRZZviwa12zMlBun2BnKpaSoIZk2lZfNhzbT/9P+HM48zC+3/MLAVgMr2qQiU9SZRRLwNBAeRH6OlHI3gJRytxAiv2D4lcDLQohYIAMYgrWUdRJCiBHACICmTZsW0azTm6V7lgKWczuIYaSTnHwoUO7DhxBO/P5gnSIFh8NNu3aCdu1iGTy4DWlpJ9C0+rnCZTWtIS6XA6/XxOVyEBsbxZgxC9C0JowcGdm+B3/c/gcNazSsEnHklZ0eeeoaVdVZxZnM0j1L2X50O33O7ZOr3W9VQOQsWxSwgxBDgSFSygeFEBrwZMBncVhKWSdsv0NSypMCyIUQdwMPAceB1UCGlPLxU52ze/fuctGifDXljCKYc7D3yb3Ur14fw0gnLm4bmZkysNyUjsNhoiiuUHc8ITKRMhuXy4HHc0GuXIxwDGMvur6b2NgoEhJmhDm2rynzrO6KwjDm5ds17kxmgWEwR9e5+Az1HZQX4WVIlrpW8NAvD+EzfVx93tV8f+P3ETmHEGKxlLJ7RAbLh6LMLPoAw4QQQ4AYoJYQ4nNgrxCiYWBW0RDItxWblPJj4GMAIcRoYEdkTD/9WbJ7CU1qNQllMOt6Ol6vDDU5EkIhOlqSlNSY1FQvS5ZksnChHyldZGV50fXDBYqFqp6Dqp7DmDELyqX6bEVjGPOIixsSisDxeH4pUDAMYwG6PhdN61OqLnXlzWLDwNB1VE2j2ylu/PMDAlE3NpZ/JSSErskPVbindWUmvzIky+5fRof3OvDTup8q2rwiU6hYSClHAiMBwmYWtwkhXgOGA68E/v6Q3/FCiPpSyn1CiKbANYD9bSwCUkqMHUbIuT1u3DGmTs3C4RCAxOl0cOeddYmPrwu4SEjYS2amAylrATLQ86Lwct7lVX22otH12bli+3V9dr5iYRgLiIu7OkxUplR6wVhsGExOTuarCRPw+/1EuVx87fFgQmjWEFyymm8YDAvcuBxCIE0T0zTxer3MqaDyGKc7+ZUheVwdyTN9nuHVua9a4eVVYJm2NMG9rwDfBJaZtgHXAwghGgHjpZRDAvtNDvgssoGH8guvtTmZdWnr2HRoE0+qTzJu3DHuuy942aJp317hscfqMGKENWt44IGDgaWpQAIGAocD0tIK77lgVZ+9psyrz1Y0mtY3V2y/pvXNdz9dn5tHVOZWarFYbBjcEhdHVmYmUkr8AF4v3yQnM2nixFy/ZnuoKnPCblzS4cCpKAghcLlcXGw7zMuEgsqQ9G/Wn1fnvsrYuWN5deCrFWtkESiWWEgpdayoJ6SUaUBcPvvswnJkB1/n/7/S5pT8vO5nAK5oewX3Pp8R9o7C6tUKCQkZdOoUAwgmTPAHlqYcgB+HA6KjBbGxPsaM2Yim1UVVC65HVB7VZysaVe2Nx/NLoT4LTeuTR1T6lLOlxWNeIJw26HtUhCDK5cIPuURvTiDM9uI8N65XkpI4lJZm+yzKkIIaLg1uM5ibO97Muwvf5ak+T1GvWvnVbCsJhTq4KwLbwQ0DJg7gYMZBlt2/LM/MAiAKRXEwalQNtm1z8OGHPqTMRogsrroqip49HcTG+khIWBqKePJ4epxSMGxyqEo+i+DMItvrRXE6ue7OO7kuPh4TTlonD1+KCi5P9SqhQFSGvhGnA6v3r6bDex145/J3Sl1QsDI4uG3KmcOZh5m9dTbP9LHSVkaMsCKWP/74BKmpEtN04HJBbGwUL7wAUjoAF4oSxdNPx6CqUYwZszFQhTaYS3HQFosioqo9K0QkSlLKo5uq8oXHwzxdp3cex/YUj+cknwVAL1UtsUhA5ekbcTpwfr3zcQgHe47vqWhTCsUWi0rI7xt+xy/9DG07FADDyCYtTSEpKRaQJCdnAwqpqSJQ2sOqpSRlDCtWmOj6YWJja6AoTkxTQVH8aFrpemDblC3FKeWxyDBI0XUu0jS6qyrdAo+89FDVMsnFiGTfiDN5huIzfbw480VMaVaJQpO2WFRCfl7/M/Wq1aNn454YRjZxcYfxeq2mR488UouPPnJimg6cThOr5p4lFn4/PPjgcaQ8FtheF8gKFOarmH9qw1iHrq9G09qjqvnXfjqTKCivoailPBYZBteHicq3Hg/dy/kmG6m+EWfyDGXjwY3cNuU25u2Yxx2d7zhlO9/Kgi0WlQy/6eeX9b9wRZsrUBwKup6F12sJQVYWjB1rYqW7QHY29OvnY+5c630Avz+TYKkPOAfYi8+Xha4fLVKr1UhiGOuIi3sZr9eHy+XE43muXAXDMJah64vQtO6o6oWFH3DKseaj67PQtH6oaq8SjbHAMLgq7OYYntdQ1FIeKXlEJUXXy10sCnLYFpfK0tmuPJFS8tnyz3jol4dQhMLX133NDR1uqGizioQtFpWMeTvmcTDjYGgJStOicLksobAI1n+y8i3at1do3z6dDz/MRsoswI/1z2rNNoSIweXKRtPyT84rKwxjHYmJk8nKysY0JVlZmSQmfkFi4o2o6vnlcP5lxMXdFypr7vF8WGLBMIz5+ST0FSwYVqZ4UFhyoq7m5Lk5huc1FLWUx0V5ROWiCgp3jUQhwMrS2a682HdiH/f9fB9T/55K36Z9+fyaz2lau+qUNrLFopIxbf00nA4nddIGMWaMJRZJSbV56KFsfD4nVngsgAwsNTno0iWKmJgjgSZHZmCWYUW5CeEjKal5uc4qrBnF6IBQgBAmpnmC6dMXM3v2cjyel8tcMHR9UVjDJB+6vqjEYqHrs/LkXswqUCysTPHLw4Tlt5Bg5A1bzZvXUBQfQ3dV5VuPJ5fPoqoSqRlKVWDy6sncP+1+jmUd4/WBr5PQOyFXg6+qgC0WlYyf1/1MK2dfhl5aB78foqNh+PAo/P6c2UIQKSXjxgmio6N55JGz0PUTHD/uY/XqI0AU4EVKP5MnH6JTpxhU9eQ+2GWBrq/B6/VhmhKHQ9Cy5Tls2rQ50HDJh66vKHOx0LTugYZJ1hKYppU8olDT+uXJvehX4L75C4slFj1VlR/CIpRKenPsrqpFFoklhhGKlOpaCW/Gp3up8oMZB3nk10f4YsUXdGvYjeSrk6tMSfKTKMuerSV9nKk9uLcc2iJJRDr6vCEJNkUVUvbsaUoh/BLM0Pbw5w6HKRXlmIS9ErZLWC5hWeCxSDocs6TbPUumpBwul8+RkrJWut3DpaLcKt3u4fLDD/+QbvfVUlGulG731TIlZXU52bFUjh49XqakLI3AWPPk6NFjZUrKvEL2M6TbXVsqilu63bVlSopR5HPMS0mRr40eLedFqH/04pQUeZ7bLVsqijzP7ZaLK3lf6tONaeumyYavN5TOF53yRf3FMu8Hj92D+8zhl/W/ACDXDg1tkxIWLgyfUQRDZa1tDgcIAX5/dmCbC6hF+/Y+atTIYtGiI4HaP6DrR8pldqGqbfF4/oWur0HTzkdV29KpUxN0fQWa1qlIswrDSEXXF6BpPVHVkrVRVdULS+3YzhmrV5Ec21am+G/5+ixOxXzD4Mow5/dPHk+pciHAyu72er2Yfj/ZXi/zdL1Szi5ON45mHeWJ359gfOp4OtbvyLRbpuVqIVxVscWiEjF722zqOBpz4khbsrFEIDiPCF+Cat0afD7redOmkJLixyq9FURhwwYH//tfbVasSAuE3TrQtNIJhWFsRNfXomntUNVWp9xXVdvminxS1fOLvPRkGKnExd0Rtu7/aYkFoyJQ1d7FLoE+O4/ze7aul1osegd8JMEw296nuQO5MjBj8wzu/OFOth/dzrN9niVRSyTaGV3RZkUEWywqETM3zuPoahUzcN8vqBLLxo05723ZAkFnds7fLPx+F2lpLjyeC0hO3obVeyqLkmIYG4mLeyMsDPaJQgWjpOj6gsCN08TrzUbXF1QpsSgJffM4v/uW4sa+xDCYr+v00jQmhWV327OKsiM9O52R00fyzoJ3aFO3DXPunIN67ul1vW2xqCTsPb6XXRmbEdseKnTfk0XkBDnLUxLIQlHMQCvVI0ycmIrXazJx4t94PJejqvk1NTw1ur4Wr9cX1vdibZmJhab1DNw4rbBXTavc9ZkiQS9V5SePh9m6Tt9S1GxaYhjcHrac9ZnHw4MjR0bYWptwjO0Gw6cOZ/3B9TzS8xHGxI2huqt6RZsVcWyxqCTM3zkfAMeu3viLfXRwCUqGXt91VzRwhMTEVLKy/GH9tveUSCw0rR0ulzOs70W7Yo9RVFS1Cx7Pp6X2WYRjJegtRNN6RMyPEWlKW7MJYH4eP8X8MvBTnMklOsLJ8mWRqCcyNmUsTWo1wRPv4ZIWl1S0WWWGLRaVhHk75uF0OHnr2a788xHLJyFljt+iIKxci+pYleEVIBuX6zhdutQkLu5XsrJMTNNyhFt+iwYlsk9VW+HxPFFkn0VpUdUuERKJ5SQnT2PChCn4/UEfyEcVKhjzDINZuk4/TaN3hG+2vfL4KXpF2E9xJpfoCCd1dyrxU+NZuW8ld3e5mzcve5Na0eWb+Fre2GJRSZi3Yx6ta3Tm2EE3jz8OS5dC586g67Bw4akFw+msxuOPN+Lo0YOAJD6+I7q+Ba/XDOU6XHppIxITu5RoVhFEVVuVuUgUhGGsQNeXoGldUdVORTxmOXFxD5GZGez3IAM+kIUREQvDWIiuz0HTLkZVexTpmHmGweCwm+2vHk9EBaOrqvKZxxPyWUR6VnEmlugI51DGIUbPHk3S/CTOrnY2P9/8M1e0vaKizSoXbLGoBPhNP/O3LyRz/h08N80SBofDEoqcaChrW/36sGcPBJecpBT4/VksXeojMfFsVDUYedEQl8sR6mdRWqGoSAxjBXFxD4eV7vi/IgmGri/B680OCIVACGdgCa1oN/ZT27Qwn/arhY87K8/NdpauR3x20VVVy8yZfaaV6Aji9Xt5b+F7jJo1ikMZh7ij8x28Puh16rrPnGrOtlhUAmZtnUW6/zhs6hdyO5imVSgwHNMMCoWJtaMDyMQ00/jzz+P89dcJ3n23CSNG1EdV6+PxDEbXd6NpDausUEDOTT+ndMeSIomFpnUNZXFbPcuvIj5+aERmFbo+J0+m9pwiiUW/PDfbflXsZnsmlegAK2l56t9TeerPp9h4aCOXtryU1we+zoUNKqffqyyxxaIS8MWKL4gya5C9Pvd0VghLIE4mmJTnxwqHPY6Um/D5JA8/vI9OnXqhqmehqvVPEgnD2IWub0fTzq0yrVTDb/pW6Y6up9zfMBah6ylo2kV4PO+GLV9dEEGbLs5TAuTiIh3XW1X51eMpM59FeRCJEh0lafRU3mw4uIFHfn2E3zb8RoezO/Drrb9yWavLAiX/z0DKMj28pI8zqdxHZnamrPNKHXn5h7dLRQkuOgXLeOR+nfthSvBLyJCwWMI0CdOkwzFNjh69Pt9zpaTslG73W1JRXpdu91syJWVnOX/akpOSslyOHv2pTElZXsh+C6Xb3VwqSmPpdjeXKSkLy9CmBXL06DdlSsqCMjvHgpQU+dbo0XLBaVSqY0FKimzsdsv6iiIbu92V7rOle9Plf/76j4weFS1rjq4p30x5U2b7syvarELBLvdxevPL+l84nHmYhEtu5YInYOzYnPeCvou8swshrO1WddlohGiMEPsAk+hoB7GxCmPGrEHTzkZVc5rA6/p2vF5/WK7E9iozu1DVTkX0U6QElqz8gRInKahq2bQlVtUeRXZsl4TidM+rShS10RNY1yBYYbc8PvvP637m0V8fZfPhzdzc8WZeH/Q6jWpWjf8jZY0tFhXMFyu/oH71+sS1jGNJndzvSQm33AJffplbMISAJ56A//1PBEp51CcpqSdpaQeJjVVISFgSyofwePqHBEPTzsXlUsJyJc4tvw9aTmjaRYElKwIJfRdVtEklpjg31apEURs9LTQMrg0Ty8llKJZbDm/hsd8e48e1P3J+vfP5K/4vBrQYUCbnqqrYYlGBHMk8wk9rf2JEtxEsnO9k2zZQlJyudwD798OTT8Lrr+cWjDp1BB6PRNdB00BV6wJ1GTNmTWD2EEzC2x8SC1VthMdzQ5XzWRQHVe2Ox/NtyGdRVrOK8qCoN9WyoCwT74ra6Cm/roCRFossXxavp7zOy7NfxiEcvHrpqyT0TqgSPbHLnaKuV2FlfKUCPwde1wX+BNYH/p5VwHGPA6uAlcCXQExh5zpTfBYTlkyQJCI/+mWejI62ypE7nVIqivU86Ldwu6V8+mnrveDrDz/MkKNHH5QpKRlSSilTUo7I0aO3yw8/3CLd7u+konwj3e7vZErK/gr+lDaloSJ8FvNTUmQjt1vWUxTZyO2W8yvIp7AgJUWe63bLBooiz42wb8M0Tfnj3z/KNu+0kSQir/vmOrnt8LaIjV8RUIl8Fo8Ba4BgmuKzgEdK+YoQ4tnA62fCDxBCNAYeBdpLKTOEEN8ANwGfFlPTTku+WPkFrc5qxaIfe4bapvp88I9/QHo6TJ9OoEwH1KkDs2ZZuRexsZkkJOzG65W4XIdJSqpJQsL6UE5FUlJ30tLST/JZlDeGsRpdX4qmdUZVq2jDlwgx3zBCTY+KU9KjKN3zABYZBoauo0age15lSbzroapMDusKGKlZxfK9y/nn7//Es9lDu9h2/Hbrb1zW+rKIjH1aUxRFAZoAHuAScmYWa4GGgecNgbX5HNcY2I41C3ECPwODCjvfmTCz2HV0l3S84JD//uvf8v77c0c63X+/lCkpMjTbiI62XgcZPfqgVJSNEjZKRdkoBw1aJxVljoQ5UlHmyNGjt+c6V0rKFjl69F8yJWVLxOy3opMmFhidlJKySrrdQ6SiDJRu9xCZkrIqYueuasxLSZH13W5ZR1Fkfbc7Ys2NgixMSZEt3G7ZRFFkC7dbLizl+JVlZhFp9h7fK0f8OEI6XnDIs145S74z750yb0hUnlBJZhZJwNNAeCPnc6SUuwOCs1sIcVLWl5RypxDidSBYI/sPKeUfxdCy05avV32NKU3+evsW+rQDl8tKwouKgvh4ax8pc/8NomluXK7DgZmF4Nprz2L27AOhmYWm5dSoMYytxMWNDystfg+q2qxUtlsZ1Y+GZVS/c1Kkkq4vDUuky0bXl56xs4s5eX6pz4lAr4pwjDxr+4aul2p2UdLEu3mGwUxdp38lyx/J8mXx9vy3eXn2y6Rnp/NIz0f4T///nFHZ15GgULEQQgwF9kkpFwshtOIMLoQ4C7gKaAEcBr4VQtwmpfw8n31HACMAmjZtWpzTVEmS/poEu7oy94fzmAs8/TQcPZrzvq5bjm4prb+6DsH/f6oag8fTEF3PQNPcqGoMnTq50PWjaFotVLVW2DibwkqL+9D1TaUWC11PzSMEqSeJhaZ1DkQlBcuMdy72eaxlrGVo2oVVWmguzpO1fXGEHdVqHke4GoHxi5t4N88wuCys5tXvEa55VRKklHy/5nuenv40mw5tYmjbobw+8HXa1Su7ismnM0WZWfQBhgkhhgAxQC0hxOfAXiFEw8CsoiGwL59jLwU2Syn3AwghvgcuAk4SCynlOGAcQPfu3U9RNq/qsy5tHVuzF8GKN0Lbfv4ZNm+2/BMTJ0JSkjXbyMqyQmVjY3OPoaoxqGpM2OvcImEY+9D1PcTGnhUoLR7Mfm5ZLFsNY1XYDbsDAJrWJY8QnFwdVlXb4/G8VmKfhWGsJi7umbDZy6sFjmFlbBtomlou0U+GsSCsgGDhvTZ6qSo/ejwl8lkUhe6qyjceT8R8FiVhZp7Z08wyqHlVHJbsXsLjvz/OrK2z6Fi/I3/c9gcDWw2sMHtOC4qzZgVo5PgsXgOeDTx/Fhibz/69sCKhqmHVp5gIPFLYeU53n8V/Z/xX8l8hqbkj5Kc4/3wZyuBWFClHj5byww+ljIrKiYAq6tJxSspe6XZPlIryiXS7J8oPP0wtkc8iJWWldLsvl4oSJ93uy2VKysqw907tsygto0d/IRXlMgkDpaJcJkeP/qIAGxdKt7ulVJRzpdvdskwztq3zzZdudwOpKHWl291ApqTML9PzVRWMlBRZy+2WMYoia7nd0qggP8f2I9vl8CnDpUgUst7YevL9he9XiezrSEAZ+ywcpdCZV4CBQoj1wMDAa4QQjYQQvwSEaD7wHbAEWIFV+W5cKc5Z5ZFSMmnFJLrFDsCZ0RgApxMSEqyZhKJYfzUN0tKsaKhgRJSuF+0cur4Hr9cMLD2ZpKU5GDlyQK7lJ8P4mzFjJmMYf59inGV5lpuWhd5T1U6MHBlf5HLhxUXTLsTpVBBC4HQqaFr+hdt03QjL2M5G140Snc8w5jNmzOsYxvxT7pdfAUHreIMxY17BMEp2/kgy3zB4Y8wY5pejLb1Vld89HhJHjaqQJaijWUd5zvMcbf7Xhi9XfskT6hNseGQD93e/H6fDTieLBMW6ilJKHdADz9OAuHz22QUMCXv9X+C/pTHydGLhroVsOLiBa5SRjHjXEgQrqQ46dSIsyc7a31qK8iOEn9hYBSvd5dRoWoNc5ck1rQGGsQ1d34ymtQDSiYv7b5jT+wVU9bx8xrkwz3JTySttGsbKsCWpjkU4wkTKrMBfP2BiGKno+nw0rVeoMZKmqXkytot/kzKM+cTFXRlWbvwnVLVXvvvmV0DQMAzi4gaGHf8nagUtwcw3DIaF+Q5+9HgivuxVEL1VtdxFItufzUdLPiJRT2R/+n5u6XQLL1/yMs3rNC9XO84EbMktZ9744wvwuZg69hqm+eHOO3PeU9UckQhy2WV+fvopE9OEhIRsOnWKQVULFgzD2I+u7yUpqTdpaZmBzniZxMV9EhKH4cNbBJzewZLfq/IVC1XtgMfz+kk+i+JiGCuJi/tnmP/hzUIFQ9dT8fuzkdLE73eQnPwjEyd+GXZDTg500+uOx/N1qXwWuj47z2xhdoFioao98Xh+yOWzGDPmlTzHz8wlFvMNo9S9tYtKWUdeVRaklPy49keemf4Ma9PW0r9Zf14f9DrdG1XdjP3Kji0W5YjP9PHLtq9g/VDM9DpkAR98YDm0PR5LKAzDml0cPgxvvQU+nz8UOmstRfkLFAvD2E9c3J9hdaEGAlkkJs4OtFe1IqKgWh6nd8EioKodSiwSQfIPoz21WOR1okN24CYYHGN+aHahqt1L5djWtL55Zgt9c71vGPPDxKEXqtozl2Nb0/rnOb5/6L35hsHQsF/6P0fol35B5TjKOvKqMrBw50Ke/PNJZm2dRbvYdvxw0w9c2fbKM7d0eDlhi0U58tfmvzjOXpyrb8UXtj0rK8cfERdnvc6pA2X11RYi6MsoeFZh+Spy6kIlJ69h4sT5ZGX5MM1oHI4sXC4H8fE9A61XV6FpHfKdVUSSkoTRqmpHPJ4kdD01EG2VzcSJ34WNkf8v/5Kgqr3weH5C12ejaX1zzSqsJaphYTOaH0+adaiqisfzJ7o+E03rn2tWMTvPL/3ZEfilf6o+2GUdeVVUjLCci0gtyW05vIXn/nqOL1Z8wdnVzua9Ie9xT9d7iFKiIjK+zamxxaIc+WLFF9Rw1qaJHMTfmBAWX7BtGyQn5xUKAAVFieHee/3Exyu5ZhWGcQJdP4GmVUdVqwd8FUrIVwFZeL1+TBMcDgeXXtqWxMS+qGpTDGMzVuWWaMoa68b/ZjF9FtZx4ft6PMkn+SxOhTUjmIWm9StwWSnnXL3y3Sf/jngn76eqar43xb55fun3jcAv/cLKcfRSVXqpKvMMg1fHjCn3JDnDMBgUJmZ/eDylEozDmYcZPXs0b89/G4dw8Fzf53i6z9PUiq5V+ME2kaMsQ61K+jgdQ2fTvemy2qiaUrlmuBQiI9C0yC/BlEJY4bFOZ+6yH8Ew2g8/lDIlJUuOHn1MpqRkSSml/PDDNOl0rpQOx3IZHb1c3n//epmSckimpOyTo0cvlykp+2RKynbpdo+VijJGut1jZUqKVQYkJWWTdLuflIqSIN3uJ2VKyqaKvDQyJWWZHD36Y5mSsiyCY86TbnesVJSa0u2OlSkp80oxTn2pKHWk212/ROPMS0mRr40eHbEyH0Upx1FeoawpKSly9OgxMiVs/DGjR0uXokgFpEtR5JjRo0s0dpYvSyYZSbLuq3WlSBRy+JThcvuR7YUfeIZCJSn3YVNKfl73M+n+YziW3YSUAmtWYQJK4B9D5NtC1ekEyCYuLg2vFxQlkyFDJD/95MXvdwAOsrKO88EHh5k4cSseTzdGjswJZ/V4biY5eSmQidWCFXR9Q1hWtx9d34CqtggdYxib0PV1aFpbVLV4SXw5Y6xF11eiaR1R1YIzZg1jOXFx94c5vz8otP2plRQ3F03rU2BSnK7PyjMjmFXo7CI/rCWqH3P5LIpL8Jd+XkraWrQo5TjKI0muoCiw/nlmU/2LOZvad2IfX638infmvxPqe/3awNfo3KBzRO23KR62WJQTk1ZMol50Q47s6IdJ3lr5uR1zQuTUg/L54OOPfWRm+pEyG79/L1OnRmMl0wsgmOzuJytLouuHUNU6YaNl8cknv+L1+vjkkxnMmPFPNK01TqeCafpxOh1oWuvQ3oaxibi4t/B6fShKNHfdpREf3xVVLXoJFsNYm09obrs8+yxD1xexbdveMOe3D11fdEqxMIwFxMVdHXaDmpKvYGhavzxO535Ftj8veZeoDMPI1z9RHBYaBteENfb5vpiNfQorx1HaG3ZR0PWZ+UaBqarKHx5PsX0WUkrGLxnPP//4J8e9x+nWsJvd97oSYYtFOXAo4xC/rP+FR3o+wqbBMUydCrlv9JJwwTjvPNi0yRIKp1OyaFEaUp7AcnY7gWwssQgeb01JhPCxbdt2DCM61NgoOXkeWVmWOz0ry0dy8jzi4zWkrAtkIKUbwsRL19cFZh0Kfn8NPvxwMRMnLsPjubPIgqHrK/OE5q7MJRaGsYy4uBF4vdkoihOnUwFEIDLr1FFNuj43zw1qbr5iYc0IphXZZ1FUrF/Tg8LE6o8SCUYkuuDNNwxm6Tr98nFkB5PkyrKw36miwAry4RTEjqM7uPene/ltw28MaD6Adwa/Q8f6RfNt2ZQPtliUA/N2zCPbzKaV7yr+75egUzu8/FXuX039+0NCgo/JkyX79h1m6dKDYe86gHQswYjGEgpfYIztfPTRicDN/doCO+Hp+jb8fidS1iA7G5KTl6GqVjZ5bGwtHA4HpukKeU68Xj/JyUvQ9dVFWprStI55QnNz/6fX9UWh2QT4uffeq2jatCGa1j3XrMIwFoflT3QLjN0nzw2qT4F2FOS0Lg0F/ZouLqXtgjffMLgizIk8LRCSGy4gZZ0kd6oosKJyJPMIr6W8xlvz3gLg/wb/Hw/0eACHKE1xCZuywBaLcmDbkW3W32Wtwlqm5j+tjoqCLl2ySUg4htcLpnk87F0fcBzwBl5nYM0KFGAPfv8JIOiH2IGqNqJLl9wVZrt0aUanTk1xOh34/T6khAkTDOLj2wMOEhL+xO+vhcPhx+FQME2J0+lgwoRZ+P1ZgWWlx04pGKraDo/nhQJ9FprWPRBKa4lJfPxQVDV3drhhLCYu7sYwX8bXqGq3QFLclEJ9FmXFqX5NF4ceqsr3RWgtWhCz8vgkZgVir4eECcgv5ZC9XdwZRJAsXxYfLPqAUbNGkZaRxk0db2L0JaNpcVaLwg+2qRBssSgHth7ZitPhZKjWgHdckJl5co8KhwOGDbNKleu6D6832Is7BjgWvmfYcwkcBY5gFf21wnEVxUFsrJMxY2aybdt+HI4oTNPE4VBIS0tHVRtz551t+fDDRQE/iB9d38S2bcfIzMxGSgVFieLuu7vStGkdtm3by0cfzQxziK8rdHahqu0KdGyr6oV4POPQ9UWB2cTJZURy13yyXgdnF3mT4iJBUcNsrV/Tf5TaZwFF74KXH/3y+CT6aVq+AlLZsrdNafLlii95fsbzbDm8hbgWcbx66at0a9Stok2zKQRbLMqBrUe2cm6tc7n4IgWPx8qnGD/e8klAjlAMHmyi6yaxsQpOZ1AsonE4YoGDSBmFlLUAP9YylAQ2AScIF5H27c/h4Yd/wO+XREU5cDpj8PuzcbkUNK0NAPHxFzBx4rxAtreT2NjqvPCCHqwWjKII4uO7oKpNGTduNkKAwyECY7Qt9TVR1QvzFYkgkaj5VFSsxLshYX6IXwoVjIqq/RSkl6oyzeM5yWeRV0AqE39s/INnpj/D0j1L6dygM7/f9juDWg2qaLNsiogtFuXA1sNbaVrbcg4H7zFLlsDChdYMwzRh6lSTqVO9OBwQHQ29esUwa5bli5AyHQifjUQBB4CdWEKRm2XLtgWK7wmys01GjLgYS1jcBJPwVLU5Hs8IdH0TmtYSXd+Kzxd0lMNdd3UNJO9tIiHhG0zTRFEEjzzSD11fBZioauuTzh0pVLVbnppPZffLM1JhtuVN3pDcXqrKL/kISEWzeNdinvU8y/RN02lepzmfX/05N3e62fZLVDFssSgHth3ZxoAWAwCr9pOmWXWeIDxM1nJmmKaVxT13rkJwtqAobhTFaqNq/fI/DOzAcnSHj2MiRFBcnIBEURx06dKMhIRZeL1+Jk5ci8dzParaCFVtjqo2D4zgyOWUjo+3MqSD0VGmaSnVm2/+jpTZAd/FM2UuGGUpEkEiGWZbUk4V2VQcCsrpqAgOZhzk6T+f5uPUj4l1x5J0WRL3d7+faGfZVw2wiTy2WJQx2f5sdh7biePY2YwZs5YFCxrg9dYOvd+sGWzbJjFNB0HByBEQgRBwzz21iI+PITn5AOPHr8fnO4j1T1cN8OFwCBwOLz6fGTjOiRBZKIrC//3flaSleQM1o4I+h+0nRUqpalM8nrtCZcyDYbKa1jYgIn4cDvD7fZhmMCR2zSnFwjDWhdWfKv3SVVlhhdn+EvEw26Iy3zDK3TFdlshAz5Z//v5PDmYc5En1SZ7v9zy1Y2oXfrBNpcUWizJm57GdmNJk0ruZ+BeuDiwP1SIYDbV9e1AYHFiRTSZXXgm//+4PrNcTqAlVnRUrDiGlibWcdARwoihR9OlTi1mzthHM3RDCycCBzUlMjENVW2AYuwI1o/wBn8O5+dqqqk1PyqVQ1ZZ4PI+j6+uIja1GQsJneL0yEBJ7foGf2zDWERc3Kiwx79+A95T1oaxQ2XloWu9CZxTFbW1aGGURZltUqoJjuqhsOLiBB6Y9wPRN0+nVuBd/3v4nFzYoeR8Um8qDLRZlTDBs1pcWizQB9gNWlVeHQ4RyGSwEoDB4MDz9tANdN9E0B6rqwDCO8vDDm/H7Y7AipI5ihdD6MIwthIfiShlF584tQiU8VLURHs/16Pp2NO3cAvMvCkJVW4ainzp1akRy8lwscSs4q1bXV+VKzEtO/ouJE78vsKeFFSp7a5iTeVKBgmFlcV8Vtu8PZR5Ca2Vt62j5ZCTPC+tXUZK8hvwim6oaXr+X1+a+xqhZo4h2RvPukHe5r9t9KI7Cm3XZVA1ssShj9C06AMrhxoGy5LlvsA4HSClCUUhCQGoqjBhhiURoHP1IIDpKkOOriMbv92KawaQ8B9bSlMIbb6ziH/9oj6o2DOzrAmrCSaVGcjCMg+j6AWJjIS3tGJrWGFVtkGcvwcSJC/B6fUycOA+P55+oaquTxtK0Drl8IOA9ZU8LXZ+Xp1/FvALFIv9KsJEOpc0psQ0QF3dpmDhNDwnGvHyWkIorGJXVMV1U5mybw30/38fq/au5vv31JF2eRKOaxftBYlP5scWiDDmadZSkeUlcfPZQ6l08kJ9+OohpNgsUEswpHHjVVTBtGmRnW7OMTz6B+HiJquYIi6bVxul0kJ1tBkp/OLEEog5CpGOV7ojCStATmKYMJOY1xDAOEBenhzVF0lDVerlsHTduKw8/vAyfLxMpDwT6ZyjMmHFVLsHIKQcSnnNhiYVVPDDoo2iHx/Pv0GvwMnHizwX2tNC03oFf18H3exd4XfNrbRpJ8pbYvm348DzipIfEIr9+FSWZXVQmx3RROZRxiGenP8u4JeNoWrspP938E0PbDq1os2zKCFssypB3F7zLocxDLPzwbnzbTBQlliuvjObHH3N6VpgmrFtnhdTOmmVt8/msZkh57x1WMTVHIOIpKCSCiy/uRN26J/jhh82BJa2gT6EJALq+L09TpJ3oegaaVhtVrYVhHOahh1bh80ms6rSWaGVl+UlOXpdLLMId3uE5F1bxwMQwH0ViIDEvx7F9qp4WVqjspCL5LPJrbRpJ8lZsFZBHnLTQvmXRr6Iq8P2a73lw2oMcSD/AE+oTJGqJ1HDVqGizbMoQWyzKiOPe47w5703aiYFs2NYpsITkp2dPa8nIKiZosXp17mOlhAULJIZBaHah60fx+WRADHJ3BqtbN4bff1+DEBKHw8eVV7bl6ad7BGYV+9i27ShOpwMrlDaaTz45hs93FJfLQVJSOyZPPoHfXx0rJDfvGnPu16raCo/nn2ElzFsF7Mvto7D6erfLc2zHk0Qi9/tFD5UtiyzuIHkrtt4eH8/t8fH5+ix6B5aQSuOzqErsO7GPh395mG9Xf0uXBl349dZf6dKw8EZUNlUfWyzKiA8WfcCB9AOM7vkcj7lEIIJIoGluNA1++imYoR0kpwqtlaQn+OUXE113oKoCTauFy+XA6zURwoXPFwWhbnvpoY54imLSs+fZIaGIi/sVr9dEURTuvfd8oCYffbQfvx8yM6N54IFjSBmNlC0RYhMOB4AP0/QRFRVFfPzJIa+q2iqXn8Iw1rFtW1qgeiyF9vWu7BRUYrugrO2yLthXGZBS8uXKL3n010c55j3Gy5e8zFMXPWW3ND2DsMWiDEjPTue1lNcY2HIg9w7uT0dPZmDZx42qxgDwxBMwdmzu46zlpWC5conXK0hOtnwXqloLj6cDun6UBQt2MHVqGiBwOCQNGtTB6XSE9adoimHsITFxcaD/tgD8NG3qQtMaMXFiGllZ0Zhm09BylhAEwm1rs2LFfj7+eD2NGgWr2haMFSL7cqD/hZN7772E+Pj++daFMozl6PpiNK0bhTU4Kvh8C9H1FDTtIsAss9yIylDSo7Kw69guHpj2AD+u/ZGejXsyYdgEOtSvuj8GbEqGLRZlwLjF49h3Yh//6f8fAFQ1BlWNwTAkY8aYaJqgTh2Bw2HNIoSwnNwHDwb9FsFYWsG4cbBnj4+nn7YEY8WKTH76KRvrn86FlJLVq734/TWBTKSMYsWKwyQkpJCZaVWVFSIq4DRuCMQwfHhrlizxsXChI+TjUBQHiYkNgGM88sgivF6reOGvv25hxoyhqOo5+X5WXV8dWn4CB02b1i9QKOLiHggLnX2f3OXIUwvtr20YC4mLuxavNxunU0HKDPz+otVzyktpMqYXGAZzdJ2LC+hSdzrx6/pfuW3KbdYPoIGv8Xjvx+1w2DOUIouFEEIBFgE7pZRDhRB1ga+B5sAW4AYp5aE8x7QL7BOkJfAfKWVS6cyuvGRkZ/Dq3FcZ0HwAFzfNidIxDElcXE6i3SOPKCiK9as+OhoGD4aHH4ac0FprhmGakqlTJb/+ms0775g8/PCeQK5FbcDyYcyalQnUBY7g9/uZPHlTSCjAEqOkpN6sWOHmoYf2YJoOnE4nLpcVgSWEYOhQBytWpDN58n683pzwWiviaVdILAxjYy5/RWxsLELUxuHIwOUy0bT2+V4XXV+cpyPe4pBYGEYqcXHxYaGpyfkKhq6nhCrRmqYJmEhZcD2ngvIfSpMxvcAw+EfYsVM9ntNSMHymj//O+C+j54zmgnMu4JvrvqFdvYLb49qc/hRnZvEYsAYr/RjgWcAjpXxFCPFs4PUz4QdIKdcCnSEkNjuBKaW0uVLzcerH7Dm+hy+v/TLXdl2XobLjWVnwxhsS07RmF0lJVm5FsAqtRXgnPROvFyZPzg74ObLz7COwSnxE4XJJOnc+mz/+2BEaSUonqak+Pv54f+gcPh+MGFENkEyYsJ8ff8xm6lQRGKs9sBQ4gtOpoGlWzLxhbAy1XHW5nCQl3UJCwu+YZi0UpTZJSUMKLOuhad1y9bDQtBxHtq7Pz5NjMT9fsdC0i0KVaK2ZhdXRL796TvnlPwisbOnt27aVOGN6Tp5IqTm6ftqJxZ7je7h58s3oW3Tu6nwX/zfk/3BHuSvaLJsKpkhiIYRoAlwBvAz8M7D5KkALPJ8I6OQRizzEARullFtLYmhV4e35b9Pn3D70b5a7KY6mCQKN0QDw+0XgL0yaBPPn52RyOxxwwQWwfLnENLMBE0WBzMworAmdxGqClNOWNSpKcvfdrenSpSaTJ28gKCCg4HQ6gdr4/cGmSdFAdbp0cZOWdgSfT2KaMVi1qYL1qWKBwzgcMnSevDkWkycvDxUZFALS0nKpXS5U9QI8nvfz9VloWq88ORb5Lyepag88nslF8lnkzX/4IjmZSRMn4vV6URTrmpjWGh11Y2MLtDsvF+eJlLr4NAuVnbllJjdNvokjmUf45KpPuKPzHRVtkk0loagziyTgaawU4CDnSCl3A0gpdwsh6hcyxk3AlwW9KYQYAYwAaNq0aL2eKyMH0g8wpPWQkxrMq6rA41FITpbMmiVYvTrn/dmzraUisP4KAStWgMPhAJyYph+fz2TWLAWoh6JU56abarN+/X5iYkzat3cRH38ukEVc3JdkZfmxbvBn4XBEccMNrdmzJ1iJNhpoiJSChATBI4+4QzkfFoeBbKS0hMXnMwPLUA1OyrG49toLmD17R9hsobCGSBegqhdgGMsZM+aTMNEwGT58GCCIj7+2QJ+FNUYPVLVH2Ov8hSVv/gMQEg+AwVdeyS/TpuHz+3kiIYEOnToVKaKpp6oy1eM57XwWpjR5dc6rPD/jeVrXbc0ft/1Bp3M6VbRZNpWIQsVCCDEU2CelXCyE0EpyEiGECxgGjCxoHynlOGAcQPfu3WVB+1VtBBMnCrKycm8N/MANNBjK6XEBYK3e5fy6B4lpOvn6a8sn4XIpvPJKG1S1Bg884AlzagOYmGYMkyYdA2oA1QMPK6nP64WlS10IkRGIirLO43Acwuk8hN8fbHZkLUNZORaP5/JZdOrUmOTkBVgtXwueWQTJ6+hOSnqMhIR/h17Hx19dymtskTf/QUBoZuFyuTinQQNM0wxU0LWWoooa/tpTVU8bkQCrlHj8lHimrZ/GDR1u4KMrP6JWdK3CD7Q5oyjKzKIPMEwIMQSrgl0tIcTnwF4hRMPArKIhVl/PghgMLJFS7i29yVUXXbf8FWY+0ajh24JRUhBekdYf9r4P00zHNMHrlej6UVasSOOjj9JCN33rb10scYgOPJzkhMJKhBB07iyYMUOQnW0JhaJk8N57rejUqSW6vgtNa5QrgztvjgX4mDhxZqBW1Fw8nsfzrRWVcw1yO7onT/4tT/vUgmtCFZe8+Q/h9Zck8FmYeFTF4n2RYMHOBdzw7Q3sOraL/w3+Hw/1eOikWbGNDRRBLKSUIwnMCAIziyellLcJIV4DhgOvBP7+cIphbuYUS1BnCocP5y8U4fj90Lw5bNkSvtWBlbVtNTd64gk/b7/tDTh6BbGx8PDDa/D7awDtyBEEF9ZsIQurQq0EslGUY0BN/H544w0FcCNEOg5HNu+915IRI6yyDScXETyZU9WKyo+8ju7Onc9jxoyZSOnA5YoiNvYsxox5L1Dyo2uh5y8Oeesv/RYmHqd7Ul1e/KafsXPH8h/9PzSu2Zg5d82hZ+OyrdxrU7UpTZ7FK8A3Qoi7gW3A9QBCiEbAeCnlkMDrasBA4L5S2lqlMQwrAiqHgn+9bc0TAmA1Q3Jg1YWCo0ejkDIG8OH3C95+e2dgZqBgzSaCHA+cpz5wNpCFokRz5ZVufvzRKo9uLeG7sWYj0aSmSh54YAN79hyhQQNJfHwzVPXsAm0tqFaU9ZlXnlQLKtzRbfXHeAq/34fD4eSRR+4gIWFUnjLlkRWMcPLLvF5sGMzTdXprGt1OUwHZcngLt0+5nTnb5nBDhxt4/4r3qeuuW/iBNmc0xRILKaWOFfWElDINK8Ip7z67gCFhr9OBooebnKYkJ3vx+6PICXcNhryejJQFv1YU2LMnC7/fgZROfD4vq1cHl6iCyVLBcziwfBXVsAShGlJmsWvX0UBUlQjb14lpevnoo+34/VsBK2Xmk082MGNGXL6CYRjb0PWdJCXdQlrakVy1ogxjJXFx/8y3f0XQ0T1mzNt4vdmYph8hJEuXrsynTHnZiUVeFhsGt4aF207yeE4rwZBS8unST3nst8cQQvDZ1Z9xa6db7WUnmyJhd0wvN4KhrqXz3ft88OuvNVCUaggRXG7yYQmFgsORI0aK4g/cCILnzsQ097Jw4V6k3JMrLBb8CAF+v9V7IojXa6LrJ7ujDGMbcXGf8u9/e0hImB4oS56z/KTrS0/qX5GX2NjYQEtYawnq2muH4HK5UBSl0DLlZcG8QLit6feT7fUyT9fL9fxlye5juxn21TDu+vEuujTswrL7l3HbBbfZQmFTZGyxKCfi46NxuTYixG6czh1ER+eEyxYHKcHnEwwZ0ogePZricChYq4DWDV6IvQhxGEXZxxNPNCUqSkGILJzOTfTsmRlY0lIwzaNIuR0hjmM1U/LjdILTmUF4gySXy4GmnRwVretb8vT13pLrfU3rjMsVhaI48u1fYRhLSEh4Bb8/BofDTVLSaEaMuBWPZxKjRv2zzJeg8qN3INxWURSiXC56nwZObyklX674kg7vdWD6pum8ddlbzBg+g+Z1mle0aTZVDLs2VDmhqtXR9Ubo+nE0rTYg0HWIjbWyt1evtvItwkt05F2Oytku+fFHBSlrIER7LAd2M2Azfn8mVo0oOHo0GytqWaIoJnffHcPixScCswcTKTOBg0A1hFC4+24X8fGtSE6unq/Pwlp22oymtUDTmufp6908z+fteMr+FcGsbdN0IIRCWtqxQA9uA01Ty10oALqpKpM8ntPGZ7H/xH4e/OVBvlv9Hb2b9ObTqz61S3bYlBhbLMoRVa0OmCQnHwZMunSpSVoadOkCEyfm7FeQUChKMAcjx+dh7dcMK7Q2FtiElV8Be/Yo+Hy+wGxEkpqahcPhxu8PDu4FFIQwiYkxiY+vgaq6UNXWJ53bWnb6JKy50Z14PHeg61vQtOaoavESKfNmbcfG1iYu7qYwH8dXEQuhLQ7dVLXKiwTAlDVTuO/n+ziSdYQxcWN48qIncTrs/+42Jcf+9pQjhnGMAQP+JivLxEqGPx+HQ6AoVlSSlFaORcuWsHHjyYKR0/8ivNig5Yuw1p7rIGV3YB0OBzRo4MblOh7qpQEuTDODoFNbiCiiogR33RVDly4+dH0PUBtVrX2S7bq+OSxE1oeub2bkyP4FisSpHNwWDoYPvx4wiY+/KleRQCvfwoiIWBjGvLCSIOXrA6kIDmUc4tHfHuXz5Z/TpUEX/rr6LzrWL7jhlI1NUbHFohzR9aN4vSZWdJKVwxDMu3A4CPS9hmuugTffzFtYEJyBfy1r+2GsfIoMIJOrrqpNgwYKEybsx++PwuVyEB9fn/j4Ruj6MTStJhDFxImZeL0Sp1Nw5501iY+vDmQSF7car9fE5XLg8VxwkmBoWotAiGywtEeLQj5rjoM7KyubxMTxJCbeiapeiGEsJS7unlDUUXz81WiaGioSaPk4Sv/r3jDmERd3eVgo7m+nrWBIKZm2fhr3/Xwf+07sI7F/Iv/q+y+7OZFNxLDFIoIczjzMsaxj1I45+Zc5gKbVwumsTnZ2C4Jhrg6HJDpakJQEaWmWDyMhIW8XPYuhQ6FBA1i92mTOHBemuQcwcTj8DB4cxYgRdYmP74CuHyQ2NgpdP4im1WXkyIahMTye+uh6JpoWg6pGAzBmzF68XjPUo1vXj5wkFqraFI/nzpDPIr8ZhWGsQ9fXoGnnhxzcWVlWaOz06XOYPdvA4xmHri/MEyK7kJEj78Xj+SrMZ3HqWYVhLEDX56JpfQpsr6rrs3IVE7TKmJ9+YrF412Kenv40f23+iw5nd+Cnm3+ia8Py9/nYnOZIKSvdo1u3brIq8vXKryWJyDlb5xS4z/33H5NCnJCQLoXIlIMG+WRKSs77o0dLqShSWotQOQ8hpIyKst5zu6X88MNs+Y9/7JGKskQ6HIul271EpqQck1JKmZJySLrdf0pF+V263X/KlJRDp7Q7JeWwdLtnSUXRpds9S6akHC72Z09JWSvd7uFSUW6VbvdwmZKyVqakrJCDBj0mHY5uEi6UitJVjh49XqakpEq3u5tUlAuk291NpqSkFjL2fDl69BsyJWV+6LXb3UgqSj3pdjcKbT/5OEO63bWloril211bpqQYxf5clZmNBzfKm767SZKIrDe2nnx73tsyMzuzos2yqSCARbIM78v2zKKELN61mJTtKaHXQgi+W/0ddd116d3k5F+vhrEXXd9Nly5NiImpHlhuMUlMhHB/qqZZy015ZxZSWo2KwCpznpbmpEEDE9O0GiBZNaKOo6o10PWDeWYKB1HVOgV+FlWtjcdzAbp+BE3L32dx8ufZg67vRtMaoqoN0PU1oY55lk9jDSNHXkVi4p3Mnm0E/CZOYmProOuLSUp6lrS0Q2haD1S18ynOs4C4uH+ELSVNRdfn5pkxzM13dqGqvfF4fjvtfBb7T+znpVkv8f6i93E6nDzX9zme7vO0XfzPpkyxxaKIpGen89fmv8j2Z7M/fT+P/vooWf6sk/Yb0XXESW0nDWMvcXHTAj6BJSQlDSMtrTaa5gB8jBlzPLQspKpW17ypU/O3I+jXiI01SUyMCRUOdDoFmmbVdNK0urhcjpAPQtMKL+WgqkUTCevz7CEu7me8XhNFyeSuu5rSpUvdPD6N8wPjXhhYelpEbGwdEhLGhjm9x6GqF57yXLo+J48wzEHTLs5VflzT+pzic/U+bURi97HdJM1L4v1F73Mi+wR3d7mbRC2RRjUbVbRpNmcAZ6xY/H3gbx7//XH8pp/eTXqjNlHp3aQ3Z7nPOmnfVftWccN3N7B6/+rQtg5nd2DaLdOo7qoOBJbzkNSrVu+k45OT/yYz04+UkJHh5+OPl5CU1AdwEhe3NxSt5PGcE/IjhBMsXe5wSLp0gbvvhrQ0E7+/GtAcIU5w5501UdVgAcA6eDzdQz6LU80qSoKu7w7MXNLx+3fw4YdbiYmJIinprsBs4fxcHfNU9UJU9ULGjPk4T2vVRYWKxcnCcDGq2hOPZ0qhPovThQ0HN/Da3Nf4dNmn+Ewf17e/nv/2/y/nn31+RZtmcwZxRopFtj+b67+9ns2HNtO6bmtenv0yprTCkppXb8ElbQZQK7oWf2z6gxZ1WvDX5r+oFV2L72/4npZnWQ1+2tVrR4wzptBzjRuXyrhxi5GyRmjbggXbufjiuQwb1oqsrAxM8yiZmbVITBRce201du2qhlUN3gqRveoqaNBAMmECLFqUzZIlXv75z6hA571quFzViI/PHfWiqnUiLhJBNK0hLpeDzMyMwHomeL0+0tKyGTnyqlMc1z1Pa9XuhZ7LEoapoRlFUBhUtWeZiMT8sL7dRW21WlYs2b2EV+e+ynervyPKEcVdne/iyYuepFXdgqv62tiUFULml/1VwXTv3l0uWrSozMZ/PeV1nvrzKX646QeGtRuGZ/Ychtx7C9kNjiPO9VLzfCfp/nT6NevHmgNraBZ9PtqR57hyQMdTVmDNi2HsoF+/z/H5TKzKKtFYpTQaYWVdp2OVHneRU/SvPlAPOBchrB7d773nJTU1mw8/PIZVl9GHwxHDsGExNGhQg/h4N6pavpVbDGMPycnL+eQTDz6fP5Codz+q2jxsn1VhGdwdAtuWoeuL0LTuhc4qypv5hsHQsEKCP3s85S4Y6dnpfLf6O8YvGc/sbbOpFV2LB7s/yGO9H6NBjcJLxtucuQghFksr0apMOONmFunZ6bw06yWGtBnCsHbDAFgwZzn+DVHItbVxKApPv/g4T4y8h2hnNIaxn7i4P5mftYPXlZ08/ngD6tTZE6h1VCcsg7nJSefS9a34fF5yCvMdBxoCmcBucirP1sPKvUgHthIsRS5ENUzzBA8+mIEQPixhl4AX0zzC1KnVcLu3Ex/fjtwdb8seVW2AqjYgPr4tur4RTWt1klDExT0R5p94A1XtEFqSqozk7ds9W9fLTSxSd6cyfsl4Jq2YxJGsI7Sp24axl45lRLcRBYZi29iUJ2ecWPzfgv/jSNYR/nXxv0LbNK13qPSEojjYvn0nSxauRFW7oet7yMryY5pgmiZjx67F4ZhOVNREpOyC318Ll0vB47n9JME4fHgP4R3uLLKwEuly2qRaYlIt7PVxwI1pHgWi8PvNsP3BytHIBk7g9Qp0/RiqWj5iYRibwtqqtkRVm+cSiSD5VZ0Nzi4qK3n7dvct40KCmb5Mvl75Ne8teo8FOxcQ44zhuvbXcU+Xe+jXrJ9dEdamUnFGiYXP9DF69mgGtx5Mn6Y5ETSq2hWPZxLJyZP55JOv+eijL5k48Ts8ni/RtKaB6rDBm3U2plkXr/cQkIaUNUNVV8PFwjCW89ZbbwLnAjlrzIMGVWP69J2YphsI1oE6DoT/ejSRcg/W8lQU1vJVEIH1z+YDdqMosYHs7LLHMDYRF/d2WH2ox1DVlvnuG0zKC84s8ladrYz0UlV+DuvbXVazis2HNvP+oveZkDqBtIw0zqt3Hu9c/g63XXBbvgEWNjaVgTNKLI5kHuFI1hEGthx40nuq2hVdT8Hn8+eqT6RpsQixBatIXzbWLCANp1NBiFh8vkyEyCI2NrfPQNeX4POlAxux/BENgGz++OMP4EDgdS2sHlJHEKIPDkdD2rZtxtq1WYEyIH6sWYgAziKnuRFAOkJkcdddNcpsVmFVmc0pFJh/C9X8xUJVO+DxvHGSz6Kyk7f1aqQwpclvG37jvYXv8cv6X3AIB/847x882ONBBjQfYM8ibCo9Z5RYbDm8BaDAWv751SfS9Q1IeRg4gRDRCLEMKVciRCYJCXfy1lsz8PtNEhI+pVOn+qGKrbGxMUi5D2tGshvog3WzX0XOUpOVROVwCC69NJrExKFMnbqetWuXAy2wHN7BZkbHUJSzAlVnTRyOY0RHK8THn+wriQTB5kbBEuQezx2nbKGaH5aPomqIBEBGdgZfrfyKicsm0rR2U+7ofAdacw2HKHnwQKYvk+RlybyW8hobDm7gnOrn8Hy/5xnRbQRNapXNv52NTVlwRolFYf/pBV7uGj6IPXvSUPDxbfJ4OnS5InCDzMbh8OL3L0fK3fj9CkuXLsc0rSxqK2/gb1S1NYaxjMmTfwmVGhfCEgyrj3ZwOSudoBiYpsm1157HihXbGDv2c4ICI8SgsMqzPq68MouePZsSG+sjLc2Fpp2Nqp6c1xEJ8mtuNHJkPzyex3L5LE4Xlu1ZxrXfXMvGQxtpF9uOpXuW8tnyz2h/dntm3zm72D2qj2Yd5YNFH/DWvLfYc3wPPRr14Mtrv+Sa86/BpbgKH8DGppJxRolFcD34UOahk96bZxhcHhdHVlaW9csd6zd9HdcE/ve/yexLiyE2NpOEhE/xehVcLhfXXnsRs2f/EpY3cB6GsYy4uHvJyjoRKDnuIDraxSOP/IPXX08O9KIAOEaOYzudjz8ez/HjF2AJhQvI4KyzFnH4cBtM08TpPMHTTw9GVYNd68o2a7eg5kaWU/v0EQmAT5d+ygPTHqCuuy6/3fobg1oNItOXyTervuHuH+/mOc9zvD/0/SKNtff4Xt6e/zbvLXwvtOQ56ZpJ9lKTTZXnjBKLYOJd8G84s4L9lwM1w4NBrZnZ2RxPW8HIkSMB6NTpF3R9NprWF3AxfHgroA7x8XGoamvGjBkfGEfB4ajDpZd2IzHxUSCb/0v6N6ZXInCSQRZQFyuMFhYsWIIQu4COQGNAcPDgMcAAaiBEDJbPpHywqsyWvLlRVWDN/jWM9Izkh7U/cEmLS/jy2i+pX90SY3eUm+Gdh5O6J5V35r/DXV3uokfjHrmOP5x5mM2HNrPj6A72HN/D4t2LmbhsIlm+LK5rfx3P9HmGbo3Kv4GTjU1ZcEaJxd8H/gag/dntT3qvXyBsMjizCP4GjImK4qKwEMpgrSHDWEJc3G1hPRmsG4mm9QjrABdDYuITqOqFvDpmDFG+TM7FJOjJ8HEAHzWxHODVkDIW659EkNPgqDYQQ3Y2JCevzDefo6xQ1aanpUhsP7Kdl2a9xPjU8VSPqs6YuDE8ddFTJ9X0AnhBe4GvV33NXT/eRVyLOLYc3hJ6HMk6kmtfl+Ji+IXDeeqip2gT26a8Po6NTblwRonFkUzrP3d+68+9VZXfPB5m6TqxsbEsT01FAW6Oj6dHPtExuj4vT0+Geahq10DhvI/Q9YWBiqpWAlo/TeP/HA6yTRM/wawKH8fFcaQzFp+vZqAo4BGspkZB/0pQPGTYNpuSsP3Idl6c+SKfLvsUgeDhHg/zfL/nObt6wVn5tWNq8/blb3Pjdzey+dBmmtdpTvM6zenbtG/o+bm1z6VhjYacU+Mc2x9hc9pSZLEQQijAImCnlHKoEKIu8DXQHNgC3CClPMkZIISoA4zHWl+RwF1SSqPUlpeAo1lHAajhqpHv+71Vld5FDJsMT+SzIqesyqZ+08+Dy+8gXovPlakcAwwdOpQ/fvgBpAzNGxoJP32u6MsJzmLatC1kZx8HViHE2QgBptkGMImKchIfX3btMa2S4zvRtMao6ulVVmLfiX2Mnj2a9xdZfocRXUfwdJ+naVanWZGOv6HDDVzR5gqqRVWz/Q42ZyzFmVk8BqwhGO8JzwIeKeUrQohnA6+fyee4t4HfpJTXCSFc5KQqlzvBKp2/rv+Ve7vdW6qxrES+z9H1eWhab1TV6ky2YOcClu5ZytI9S3lcfRyAVMPgjoDzPEo4iJJ+srHmC9VNk1k/fMfRmBj++fgLzNBXEBNTg/bt69OlS0dSUzOxfCJdUdWycWpbJcd/DAuTHXZaCMbhzMO8nvI6SfOSyPBlcMeFd/Cf/v8pskiEE6wubGNzplIksRBCNAGuAF4G/hnYfBWgBZ5PBHTyiIUQohbQD7gDQEoZXiip3OnbtC/dGnbj3YXvFioWRak+ai075W5f+cPaH0LPpZQIIVig62RlZSFNMxTrFFxoEsAxKcnIErz51if4/RLT9DN7djqKEsW7777MiBFDS/W5C0PXd+YJk91ZZcXClCYzNs9g4rKJTF4zmfTsdG7scCMvaC/Qrl67ijbPxqbKUtSZRRLwNLmr1Z0jpdwNIKXcLYSon89xLYH9wCdCiAuBxcBjUsoTeXcUQowARgA0bVo2TlUhBK3rtmbu9rmn3K8k1UdTDYMFus43UV+Htu1P30/96vXpqWk4FAVfQCz85Fz4/Q4HmQgk1fD5TIJVgKWshs8HDz/8Ep06nY+qdinFJ7cwjMVhM6GcKB1Na5wnTLZxqc9V3qxLW8fEpRP5bPlnbD+6nVrRtbi106082ONBOjfoXNHm2dhUeQoVCyHEUGCflHKxEEIrwfhdgUeklPOFEG9jLVf9O++OUspxwDiwSpQX8zxF4rj3OD+v+5mbOt50yv3yVh/9KjkZQ9fpo2khZ/cSw2C+rtNL0xDAHXFxHK+RxeaHTNTYXhhp89l0aBP1q9eni6oy8v/e58mHEsjw+alORshV3ezc5izdcRC/GYWUJg6HIywXQ+D3S3R9QanFwjAWExd3a1h70kkhwVDVBng8w0rts7DKg2xC01qWSxTV4czDfL3yaz5d9inzdszDIRwMajWIsQPHclW7q3BHucvcBhubM4WizCz6AMOEEEOw/LS1hBCfA3uFEA0Ds4qGwL58jt0B7JBSzg+8/g5LLCqE71Z/x4nsE9zR+Y5837d6LSykfmzjnOqjisI3n3yC3+cjyuXie48HBbg9OPNQFDp07ow3K4ujna0cje5HzsfAEotgP+7bR9xN606d+CL5C34b/wH4vShSsnnrHrzUBgSCY7Rs2ZhevXrxzTcz8Psl0dEuNK30TX7yj97KmV0ES46XFKs8yMdhRQbvLhPB8Jk+/tj4BxOXTeSHv38gy59Fh7M7MPbSsdx6wa12i1EbmzKiULGQUo4ERgIEZhZPSilvE0K8BgwHXgn8/SGfY/cIIbYLIdpJKdcCccDqvPuVF5NWTKJ13db0OdeqOGsYqej6fDStF+AgLu7e0C/v/yV9wKG0nezeto1JH32E3+8Hr5e5uk404PV6we8Hv59VCxbgF3CsM8TsFdx8y3D+5/mUTYc25Tp/sLvb0vgb+V9iIjOmT0ea2QgkkmwkR9i48SA7d67n8cefYOnSv7n22isisgRVUPQWWDdggcg3zyAvhrEcXV+MpnVDVS8Ibdf1TWFFBn3o+qaIisWKvSuYuGwik1ZMYs/xPcS6YxnRbQTDLxxO14Zd7SglG5sypjR5Fq8A3wgh7ga2AdcDCCEaAeOllEMC+z0CTApEQm0C7izFOUvF3wf+Jq5FHEIIDCOVuLj4kDgMH35drl/e+9K8jBw5koWGwTcTJ4LXS5TLRR9NQwFcLhf+zMxQGOzxHoKscyRjLxiFerFGw/kNTxILgLWGwSZdZ9i11zJz9mxiMjOJlWkcohp+LKd4ZmY2b731HqYpmT17Fp06tUVVe5w0Vn7MNwxm6Tr9TnLMexk+fBDg4vbbb6J+uzr834L/49cNvzJzy0yqRVXjX33/xWO9HivwxmsYy4mLeyCsodH7IcHQtJaBGlrB0ielLwmy/8R+vljxBROXTSR1TypOh5OhbYcy/MLhDGkzxM5psLEpR4olFlJKHSvqCWnV1o7LZ59dwJCw10uBMmv1V1RMabLn+J7QMoWuz88lDmDm+eVt3Zx7qCrfezzMzeOz+Mzj4YfkZH765BOyorLZN8CkZ91uPPkPq6lSy7NaniQWaw2DxLg4fF4vTpeLN5KS+P7XGXw1dQN+HMAyrF4WDnw+P1LKQKn0uUUSi/mGwZAwx/wvAce8YRjExV1GVq1MxMWSr/SPOfznYQBandWK4RcOZ+nepTz+++M0r9OcQxmH2HJ4C2kZafhMH81qN6PFWS34669lZNU9jOmFLDOK32fMDYmFVR7k7oj4LHYd20WinsgnSz/BZ/ro1rAb71z+Djd3upl61cqmcKKNjc2pOWMyuPef2I/P9NGwRkMANK1XLnGIj/8H8fHXnJR5DZZgRAELdZ0ooLOq0jXwuDo+nsemJ7BWLmbCjRNDv8pbntUSfYuey4ZVuo7P68X0+/F5vfjT0jiv51Vk//ApijyMi9ooHCULPzgVTFMGhKsPRWFWHsf8rEBb0B9n/EzmwBPIzib4oIW/BXcPvptBrQaFylJ8vvxzUrancPXXVwOWB6VOTB0cwkFaRlrOSQIRxybwku8Rfv84mUuaX8IlLS7hou4XlUokjmYd5bW5r/HmvDfJ9mdzX7f7eKD7A3SoX3XKnNvYnK6cMWKx8dBGAFrVtbrWqWoXPJ7kkM8i6BfIrz/0UsPg7rBf7B97PHQOzDBk8xhmmwt5tNejuW5qTWs3ZcfRHaFcC4AOmobT5QrNLJyxsaz4dRYOeYiaLCQmUL68BnD3409QvU49NK1PkZeggvWtgnZ2u7gHz//1PG/530BeaCIWKkTPj+Hdn99F7Zk7FHho26G8ddlbNK3dlI71O9LyrJY4HdbX47j3OFsOb2H3sd2krlxD6oo1tDqvEdTNYsaWGbw691VGzxmNS3HRvVF3jmYdZefRnTgdTqKd0bgUFy7FRbQS9jzP9iglij83/sn+9P3c2OFGXr7k5dC/lY2NTcVzxojFurR1ALSNzWnYo6pdiuQ8/jE5mazMTKSUZHu9TE1OZo6u07t/fxL+fpp61eqRqCXmOsaUJopDybX+305VSfR4WKXrOGNjeTYhgb2ZsThRUAJCIbDyQc6pU51HRiYU6zP2UlV+8XjQZ/zFodZHuXHezRxIP8BNHW/i2jrXst69Ee3f/VDzyRmpE1OHhN75n6+GqwYd63ekY/2ODGw10ErHDONY1jHmbJvDX5v/wthh0KJOC/o17Ydf+vH6vXj9XrL8WTnPfVlk+bI4lnUs1/auDbsyasCok6q72tjYVDxnjFisT1uP0+EssEteQSwzDH6YMCGULCccDr6cMAGv309GZwd7r8zm42EfUyemTq7jMrIzcDtPjvNvp6q0U1XeGzMGr9dLtDzMUc7FjyAKaWV4u1x0urgH3676loY1G3Jx04tZbhgs0XW6ahoXnCJB8JzzG/LTml+Zu2Yu/Zv15/VBr9O9UcBldJKHKTLUjK7J4DaDGdxmcNmcwMbGpsI5Y8Ri3cF1uZZWispCXcfvt9zPQgjadOnC4sWLyVb8HND8NBVN8s3byPRlnjIprHdgyUh4M3Ap++g15B68Ubs4du4RTrTwMkgfQraZzfn1zuerrh/zUFwc2YGIrHc9nnwF44e/f+COH+7AlCafXvUp8RfG2yGlNjY2EeHMEYu0dbmWoIpKj8BNPXijvv7uu1m+YgUH+2biryl5scd/823XmuHLIMYZU+C4XVWVz6ZPZ6r+DWnN0pl/YjFLdi8BoEl2E7JNq9GR1lxjia6THeYY/zU5mWW6ThdNo6Oqku3P5tnpz/LmvDfp2rAr317/LS3POr262dnY2FQsp7VYfLHiC6Zvms6gVoNYl7aOgS0HFnuMC1WVjzweFuo6PTSNC1UVZ8ta3Dj3Nq5sdDnDB9+T73EZvvyXoaSULNy1kClrpvD939+zLnsdbIDeTXrz6qWvclW7q3jTeJPxqeN5UXuRx3o/xqa6K4gKOMYdTie/TpiA6fcT5XLx7M+T+M/msczbMY+HejzEG4PeINoZXezPaWNjY3MqRHAtvjLRvXt3uWjRolKNMXPLTC797FKklPilH4Bl9y/Dv+kEC3SdnppGF1U9RRJb/kgpGfLFEFK2p7Du4XWcU+OcfPcb9uUwth/dTup9qfhMH7O2zmLKmilMXTuVHUd3oAiFAS0GcPV5V3NVu6toXCuneF+v8b2oFV2LP2//M7Qt6LPYt20bP3/0ERk1/KR1FOwbGIPD5WT8sPHc0OGGUl0zGxubqosQYrGUssxy2k7LmcW2I9u4/tvraV23NXPunEPqnlSOe4/j33SCO8NCYJ9ISuLxhAS8Xi9Op5Nb7ryTW+Pj840WCvLTup/4bcNvvDnozQKFAqycgaV7lnLnD3fy09qfSMtII8YZw+WtL+flS15maNuh+XbsAziUcYgWdVrk2naBqnJW+yb879fXWeY3OdoYQNK1Tlu+uu1bu42njY1NmXLaiYXX7+UfX/2DLH8WU2+cSmy1WC5teSkAHwYikEy/n2yvlx8nTw4lsfn9fj7+8EOSJ07kd48nX8HI9GXy+O+Pc36983m458OntCPoSJ+yZgpD2w7lmvOv4bJWlxWpic6hzEOcFXNW6LW+Ref5v54PlVY//7y2XJbegjvVuxkcd32Rr42NjY1NSTntxCJlewqpe1L57OrPTmp20zOPs3rYtdcyY/ZsMjMzMaXELyUykPmcn1i8kfIGmw5t4s/b/yRKiTqlHe8OeZftR7fTr1m/YtUwklJyKOMQZ7nP4mjWUZ758xk+WPwBLeq04KUBL3F9h+tL5Ki3sbGxKQ2nnVgEI4oGtRp00ntdVJVPPJ6QzyITuGn4cPbt2cOvv/yC3+fD4XAQGxt70rGbD21m9JzRXHP+NaGZyqloV69diTqzHfcexy/9LNy1kI7vdWTnsZ08oT7BiwNepFpUhXWktbGxOcM57cRifdp66rrrUr96fo37LMHoEiiuNzDMf/FoQgJvv/kmpt/PUwkJdOzUid6B2YUpTe744Q4UofDWZW+Vqf3HvMcAmL5pOufVO4+5d80N9cSwsbGxqShOO7EILt+Y0sw3/yHIzDxF95YuXQpSIk0zVIQvKBbvzH+HWVtnMWHYBJrWLtsOcA1qNGDUgFFEOaJ4rPdjp8zVsLGxsSkvTjuxaFijIT7Tx4H0AwXOLgD65ym6d82112LMnh163U/TAKsHxkjPSIa2HVpgh71I4hAOnu/3fJmfx8bGxqY4nH5iUdMqQb772O5TioWqqvzp8TBT1+mvaaiqSsdOnUI5F71VFZ/pY/jU4VSLqsa4oePs0hk2NjZnLKedWASbG+0+vpsLObnceDiqquaKeuqtqqGlJ4Cxc8eyYOcCvr7u65AI2djY2JyJFLyoX0UJNjfafWx3qcb5fPnnJOqJ3NjhRjsz2sbG5ozn9BOL4DLU8ZKJRUZ2BiN+GsHtU26nd5PevHfFe5E0z8bGxqZKctotQ/2+4XcAarpqFvvYDQc3cP2317N0z1JGXjySFwe8WOyS5jY2NjanI6fVnTAjO4N7f7qXLg26cF/3+/LdZ4lhME/X6a1pdA3zT3y3+jvu+uEunA4nP9/8M1e0vaK8zLaxsbGp9JxWYvHbht/Yn76fSddMyrfExhLD4LawRLzPPR469uzGU388xTsL3qFX4158fd3XNKvTrAKst7Gxsam8nFZi8e3qb6lXrR4DWgzI9/15gUS8YCHBaTOm8MCqBBbsXEBCrwReHfhqseo42djY2JwpFFkshBAKsAjYKaUcKoSoC3wNNAe2ADdIKQ/lc9wW4BjgB3xlWW993o55xLWIK9DP0DuskGDm+Qqvyw/ggGDyDZO55vxrysosGxsbmypPcaKhHgPWhL1+FvBIKdsAnsDrghggpexclkIBkJ6dTu3o2gW+31VV+eTP32nx3z7suM5Lq3qtWTJiiS0UNjY2NoVQJLEQQjQBrgDGh22+CpgYeD4R+EdELSsBmb7MUy4jrdm/hifX/Ys/zVnc3+1+Uu5OoVXdVuVooY2NjU3VpKjLUEnA00B4POo5UsrdAFLK3UKIgmprSOAPIYQEPpRSjstvJyHECGAEQNOmJSvW165eOxbtzt2Ode2BtXy7+lu+WfUNK/atoHpUdSZdM4lbOt1SonPY2NjYnIkUKhZCiKHAPinlYiGEVoJz9JFS7gqIyZ9CiL+llLPy7hQQkXFg9eAuwXkY3HowL858kXk75uHZ5OGb1d+wfO9yy4hz+/D25W9zffvr7dIdNjY2NsWkKDOLPsAwIcQQIAaoJYT4HNgrhGgYmFU0BPbld7CUclfg7z4hxBSgJ3CSWESCwa0H88LMF1A/tvIn+pzbh6TLkri2/bU0qdWkLE5pY2Njc0ZQqFhIKUcCIwECM4snpZS3CSFeA4YDrwT+/pD3WCFEdcAhpTwWeD4IeDFi1uehR+MePNvnWc6pcQ7Xtb/OFggbGxubCFGaPItXgG+EEHcD24DrAYQQjYDxUsohwDnAlEBpbyfwhZTyt9KZXDAO4WDMpWPKangbGxubM5ZiiYWUUgf0wPM0IC6ffXYBQwLPN0EhdcJtbGxsbCo9p13VWRsbGxubyGOLhY2NjY1NodhiYWNjY2NTKLZY2NjY2NgUii0WNjY2NjaFYouFjY2NjU2h2GJhY2NjY1MoQsoSlWEqU4QQ+4GtpRiiHnAgQuaUBZXZvspsG1Ru+yqzbVC57avMtkHVsK+6lPLssjpBpRSL0iKEWFTWvTNKQ2W2rzLbBpXbvspsG1Ru+yqzbWDbB/YylI2NjY1NEbDFwsbGxsamUE5Xsci3wVIlojLbV5ltg8ptX2W2DSq3fZXZNrDtOz19FjY2NjY2keV0nVnY2NjY2EQQWyxsbGxsbAqlSomFEOJrIcTSwGOLEGJpnvebCiGOCyGeLOD414QQfwshlgshpggh6gS2NxdCZISN/UEF2FZXCPGnEGJ94O9ZYe+NFEJsEEKsFUJcVlzbTmWfEKJn2PZlQoiri3l8mV27YtiWKITYGbbvkLD3KsO1K/fvXTFsq6jv3UAhxGIhxIrA30uKeXypr12E7Cuz714EbIvs905KWSUfwBvAf/Jsmwx8i9X6Nb9jBgHOwPNXgVcDz5sDKyvYtrHAs4Hnz4bZ1h5YBkQDLYCNgBIp+4BqYdck2EvdWYzjy+zaFdU2IDG/61pZrl1FfO+KYVtFfe+6AI0CzzsCO4t5fESvXUntK6/vXglti+j3rkrNLIIIIQRwA/Bl2LZ/AJuAVQUdJ6X8Q0rpC7ycB0S8SXdJbQOuAiYGnk8E/hG2/SspZZaUcjOwAegZKfuklOlh1yQGOGXEQ36fL1KU1rZ8qBTXriK+d8W4dhX1vUuVVldNsP5fxAghoot6fKQprX35ELHrV1LbIv29q5JiAfQF9kop1wMIIaoDzwAvFGOMu4Bfw163EEKkCiFmCiH6VoBt50gpdwME/tYPbG8MbA/bb0dgW0TsC9jYSwixClgB3B/2BSvS8ZTRtSumbQ8HptsTwpZSKtu1g3L63hXDtgr73oVxLZAqpcwq5vGRunalta+sv3ulvXYQge9dsXpwlwdCiOlAg3zeek5K+UPg+c3k/oXxAvCWlPK4JcKFnuM5wAdMCmzaDTSVUqYJIboBU4UQHaSUR8vbtvzMzWdbvr8SS2gfUsr5QAchxPnARCHEr1LKzALsyXt8WV67otr2PjAK67qMwpqy30Ulu3bl/L0r7r/rSebmsy2i1y5wbAesJZJBhdhTou9dOdhXqu9eeVy7kn7vTrY+gmt+5fHAEri9QJOwbbOBLYHHYeAg8HABxw8HDKDaKc6hA93L0zZgLdAw8LwhsDbwfCQwMmy/3wE1Utcun31mFPTZi3h8xK5dcWwL26c5gfXYSnbtyvV7V1TbKvJ7h7Ussg7oE4HPV6JrFwn7yvK7F4FrF7HvXbEvbEU/gMuBmad4P5GCnciXA6uBs/NsP5uA8wloCewE6pazba+R29E4NvC8A7kdZZsooaMsP/sCYwadYM2AXUC9YhxfZteuqLYRuNkFnj+OtVZcaa5dRXzvimFbRX3v6gTGv7aEx0fk2pXWvrL+7pXStoh+74p9YSv6AXyKtf5a0PuJhN2QgfEEVBPLybQdWBp4fBDYfi2Wo2gZsAS4sgJsiwU8wPrA37ph+z2HFU2xFhgcyWsH3B747EsDn/0f+dl3iuPL7NoV1TbgM6x1+eXAj3n+A1f4tauI710xbKuo793zwImwa7IUqF/e37vS2lfW371S2hbR751d7sPGxsbGplCqajSUjY2NjU05YouFjY2NjU2h2GJhY2NjY1MotljY2NjY2BSKLRY2NjY2/99eHQgAAAAACPK3HmGBkoglCwCWLABYAem0kMZGZlDEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c4998d3a-6bc9-4198-adaa-5638c2164b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP for Staten Island tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#special for NYC - is it possible to create R-leaning districts based in Staten Island?\n",
    "counter = 0\n",
    "statenHDweight = [-999.]\n",
    "statenHDvGOP = [-999.]*1\n",
    "statenVTDGOP = [-999.]*1\n",
    "for t in range(nTracts):\n",
    "    if isDownstateTract[t] ==1 and tractGeom[t].centroid.x < -74.07 :\n",
    "        plotPoly(tractGeom[t])\n",
    "        counter +=1\n",
    "        if counter == 1:\n",
    "            statenHDvGOP[0] = HDvGOP[t]\n",
    "            statenHDweight[0] = HDweight[t]\n",
    "        else:\n",
    "            statenHDvGOP.append(HDvGOP[t])\n",
    "            statenHDweight.append(HDweight[t])\n",
    "plt.show()\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP for Staten Island tracts\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(statenHDvGOP, bins=n_bins,weights=statenHDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "95f167b6-72d7-44c1-bd8f-9c4b736357de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's plot all near-Staten HD leans\n",
    "for t in range(nTracts):\n",
    "    if(tractGeom[t].centroid.x < -74. and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "#x,y = wholeMAP.exterior.xy\n",
    "#plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ea68583-570e-4ecf-a90d-8db153d1a3ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in range(nTracts):\n",
    "    if isDownstateTract[t] ==1 and tractGeom[t].centroid.x < -74.07 :\n",
    "        plotPoly(tractGeom[t])\n",
    "        counter +=1\n",
    "        if counter == 1:\n",
    "            statenHDvGOP[0] = HDvGOP[t]\n",
    "            statenHDweight[0] = HDweight[t]\n",
    "        else:\n",
    "            statenHDvGOP.append(HDvGOP[t])\n",
    "            statenHDweight.append(HDweight[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6a6cc489-4d62-4b8d-b99c-97af419e708d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staten Island should be  0.9931566035472059 districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Staten Island should be \",16.*np.sum(statenHDweight), \"districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in NYC-LI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11282577224366033 = NYC-LI std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11977958020521161 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for NYC-LI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.3092\n"
     ]
    },
    {
     "data": {
      "image/png": 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tT/IUcAszv2bgGPAg8M/AXuAq4F3grqqauJMJzrDttzBzeKeAw8DXTn4fM2mS/BHw78B+4NNh+JvMfBcz0fv/LNt+N8t8/090oCRJy9ckH+KTJC1jBkqS1JKBkiS1ZKAkSS0ZKElSSwZKktSSgZIktfR/ptK0ce00PpoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by -0.01658 0.29359 HD avg vs true 0.31017\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.24851, should have been 0.24906\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for NYC-LI\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 3.089\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for NYC-LI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 0.199 TN-noMemphis seats out of 8\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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16cAmkhOS+coJX+nU/RUWFhIIBFBVAoEAhYWFlgCM6eHmrpkLwJQhUzptH9YZXAfbU72H3VW7uWXyLVT6Krl09KWdXsdfUFCA2+2OlgAKCgo6dX/GmM73YdGHZCZn8m8n/lun7cMSQAd7cf2LKMrNU27u1Ee4m8rPz2f27NkUFhZSUFBgV//G9AIV3goGpw/u1DY4SwAdbFnJMoZlDuuyk39Efn6+nfiN6UVK60o7ZdjQpqwzuA7WEGjotMEbjDHx46Oij6jz13XqPiwBdLCTBp7EtvJtVPmqnA7FGNNDvbPjHQCO63tcp+7HEkAHOyP/DIIaZFnxMqdDMcb0UP+34f8AeHzm4526H0sAHey0IachCB8VfeR0KMaYHirSEVx2anan7scSQAfrm9yX0QNGs6JkResrG2NMDKOyRgGwYNuCTt2PJYBOMHnwZFbuWel0GMaYHmrcwHEAndoRHFgC6BQj+4+kpLqk0//xjDG9U6Sr+M4cCwAsAXQKb6MXt7hJcNljFsaYtttVuYvb/3k7x/c7HqDTx+K2M1Qn2FW5i7yMPOtF0xjTZqrKcY+FbvucM2EO/ZL7UeYt69R9Wgmgg2wr28aL61/kQN0BtpZtjY7naYwxbXHzmzdHp+9dfC8V9RW89dlb+Bp9nbZPSwAdwOv3MvWZqXzjb98g75E8lpcs56LhFzkdljGmh5i7ei5/XPFHrj/legDmbZ4HwI6KHWw+sLnT9mtVQB1g5Z6VlHvLufucu9lStgW3uLl16q1Oh2WM6SF+/cGvAXhsxmPcN+0+znz2TMq8ZaQnpjO0b+cNhWsJoAOUe8sBuGTUJfx8yM8djsY09fHHsHgxTJsGp5/udDTGHClSbXzfufeR4kkhxZPCjv/Y0SX7tgTQASIZelvZtk4dvMG0z8cfw/nnQ0MDJCbCO+9YEjDdz/Nrn0dRrjjhii7ft7UBdIBxA8cxtO9Q/rTyT06HYppYvDh08g8EQu+LFzsdkTGH2la2jTvfuZMz889kfM74Lt+/JYAOkOBK4NbTbuX9Xe+zvHi50+H0ekVFRbz//vsUFRW1uN60aaErf7c79D5tWpeEZ0ybvbDuBbyNXl782ouO7N8SQAe59pRr6ZPYh7vfvbvTH96IZ0VFRTz33HO8++67PPfccy0mgdNPD1X7PPCAVf+Y7mlZyTJOzD6RvIw8R/bfagIQkWQRWSYia0Rkg4jc32TZ90VkS3j+QzG2zReRd0VkU3id/2iy7D4RKRaR1eHXrI77WV0vIymD+6fdz1ufvcXEP01EVZ0OqVcqLCwkEAigqgQCAQoLC2OuFykl5OUVceeddvI33Y+qsrx4OZOHTHYshrY0AvuA6apaIyIe4AMR+QeQAlwGjFdVn4gMjLFtI/AjVf1URNKBlSKySFU3hpc/qqoPd8QP6Q5+cNoP+OX7v2Rr2VbKvGUMSB3gdEi9TkFBAW63m0AggNvtpqCg4Ih1IqWEyDqzZ8+24TJNt7OrcheldaVMHtyNE4CGLmVrwh894ZcCNwEPqqovvN7+GNvuAfaEp6tFZBMwBNh4+Lq9QYIrgd9c+Buuff3aTh/KLV7l5+cze/ZsCgsLKSgoOOLEXlRUxOLFi2lsbASgsbGRhQsXkpuby4QJEywRmG4jMmiUk3cOtuk2UBFxAyuBEcATqrpUREYBZ4vIL4F64HZVbbYFVEQKgJOBpU1m3yIis4EVhEoKFUf3M7qeqvLVl75KqieVgswCbpx0Iy5xcdtbtwFYAuhE+fn5MU/kkSv/yMk/oqSkhJKSElavXs2cOXMsCZhuYXnJchLdiY7c/RPRpgSgqgFgoohkAvNEZFx4237AVGAy8JKIHK8xKr9FpA/wN+BWVY0MlvtH4AFCpYkHgN8C34mx7fXA9QBDh3beE3HttbF0Y/RxbYCXNrxEQWYBVb4q+iX369Sn90xskfaB5gQCAZYsKeTf/90SgHHe8pLlTMiZQKI70bEY2nUXkKoeBBYDM4DdwKsasgwIAkdUeofbDf4GPK+qrzb5rn2qGlDVIPAUELMcpKpPquokVZ2Und25w6O1x+cHPwfg79/4O0uuWUJJdQmLdizix2f8mC23bCHVk+pwhPGnoKAAl+uLP+nIpciuXXksWXIWhYVDuffeAj7+2KEAjQkLBAOsLFnpaP0/tKEEICLZgF9VD4pICnAB8F+E2gWmA4vD1UGJwIHDthXgGWCTqj5y2LLccBsBwBXA+mP9MV1p4qCJAKzeu5q7zrmLTTdvYn/tfk4dfKqzgcWx/Px8Jk6cyMqVK/F6E0lM9LN79xD+8pfZNDa6EQERF4sX211BxllbyrZQ3VDteM8BbSkB5ALvishaYDmwSFXfAJ4FjheR9cCLwBxVVREZLCJvhrc9E7gamB7jds+HRGRd+HvPA27ryB/W2fIy8rho+EX8ftnvqfPXkd83307+DisqKqKmJnS/QnJyA/v25bBkyTk0NiYALlRduFz2QJhxXuSBUSdvAYW23QW0llDj7eHzG4BvxZhfAswKT38AxBwVRVWvbm+w3c3d59zN2f9zNk+ufNJ6/3TY4Q3AK1eewoIFs1CNXOMobrfwxBN29W+ct7xkOX0S+zA6a7SjcdiTwMfgrKFncV7BeTz04UPUN9Y7HU7cOvzWz6KiPN58M3LyF0BIS6vh619fzcyZLXcfYUxXWFa8jFNzT8XtcjsahyWAY3T3OXezp2YPz3z6jNOhxKXIlf+OHV90n/v55wWohk78IcrkySsYNWo+f/7zn1m5cqUjsRoD0BBoYM2+NY7X/4MlgGM2rWAaZ+afyYMfPtipQ7eZ2GLd+jlsWCEuV5DQjWlBRIIMHLiXYNBFIBBkwYIFrXYkZ0xnWbtvLQ2BBsfvAAJLAMdMRLjrnLvYXbWbVze92voGpkNFuoYI3XAW8sWTKILLpSQne9m7dzAiiggEg8qaNWscideY7tIADJYAOsRFwy8iPyOflza+5HQocSfSNcQpp5yCy+WiujqNjz46nUDADQjBoAuvtw87dgwjEHATCITmGeOUZSXLyE7N5ri+xzkdiiWAjuASF+cffz4f7PrAegF1yKpVqwgGg/TpU8s557wfnvvFv8Xu3fm88spX2Lx5DFVVmUyYMMGZQE2vV3iwkLFPjOVPK2IPEBXpAbRpqdUplgA6yKTcSRyoO8Cemj2tr2w61Jo1awgGgwCIQG7uXiZMWB1e+kVD8Nato5k37wrq6vo4EaaJE8+uepZNBzZx44IbkfuFX73/q+iymoYaNh3Y1C3q/8ESQIfJSMoArBM4J9TW1h4xb/jwz8INwZFSgKDqIhBwsWPH0GbHETDmaJV7yymuKuaBJQ8cMr9pAlhZspKgBrvFHUBgCaDDuCR0KIMadDiS+JOWlhadVgVVISHBT1ZWaZO1FAjgdgcZPrwo5jgCxhytvTV7GfDQAPIeDY3s9fNpP+ef3/onY7PH4na5o1XDH+z6AMBKAL3NxtLQEAdZKVkORxJ/cnNzo9OqIKKccMJWbrjhabKz9yMSSsouF8ycuZDrrz/JuoQ2HerOd+5Em7Q5XX/q9Vw4/EK+PfHbVPmqqPJV8fBHD3PXu3fhcXnITuseHVu2qTto07LK+kqeWP4EV4y5gqxUSwBdqaioiIULF0Y/hzp8C027XAFSU+uiD4WpBqirS2XPHmunMR2j2lfNqU+eyrbybfznWf/J1ROuZmjfodHegCPvdf465m+ZD8AtU25xLN7DWQI4Rv6An2vmX0Olr5K7zrnL6XDizuEPgol88RxAMCjU1aUiokAQtztIXl4RCxf6mDChyEoB5pj97F8/Y1v5NuZMmMPPz/v5EV07RMYFGfzIYAAePP9BfnrWT7s8zuZYFdAxumnBTby2+TUen/E4p+Se4nQ4cSc19chxFyIlALdbOeGETaSk1JKeXs3YsRv417/OY/PmEdYIbI5ZubecZ1c9y9fGfo0/X/7nmP36nDbktOh5ISsli++f9v2uDrNFVgI4BjsP7uSZVc9w29Tbut0/bDyIVP/EevZChPBDYWeGHwpTRBSXK8jxx++ioOCCrg/Y9CpPLHuCWn8t95xzT7PrZKdls/L6UN9Tqtot7v1vyhLAMXjn83cAuO6U6xyOJD61NASkKmzfPpxAwI2qC5EAWVnlXHbZ30lI6F7/CU3P42v08fiyx7lk1CWclHNSm7bpbid/sARwTCq8oTHsh2QMcTiS+BTpBygQCOByuZg4cSK5ubksX76cffv2MXToLr71rb+yZ88gGhqSGD16Cy6XEgwqhYWF1gZgWuQP+PG4PTGXrdq7igN1B/j2xG93cVQdyxLAMahuqAagT6I9WeqESD9AhYWFFBQUkJ+fT1FREfv37wcgK+sg/fsf5PjjQ+M3Ry7ARMSeAzAtOlB3gPF/HM/1p17PfdPuO2L5suJlQKiOvyezRuBjUO2rJs2TFn0IzHS9/Px8zj777OjVfGFh4SFtApHbQpuWvseNG2dX/6ZZqsrV865mT80e7n/vfg7UHThineUlyxmcPrjHl/7tzHUMqhuqSU9KdzoM00RBQQEuV8t/1rG6jjAm4rk1z7Fw+0ImDZ4EwMdFHx+xzrLiZd3mad5jYQngGFT6Kumb1NfpMMxhBg0a1OLysWPHdlEkpqfxB/xc+/q1jM8Zz2v/9hoA1/39ukNKlQfrD7K1bGu36c/nWFgCOAaV9ZVWAuhGIsNDlpSUNLvOcccdx6mnntqFUZme5BdLfkFAA9w29TaGZAxhxogZ7Kvdd0g10IqSFQDxkQBEJFlElonIGhHZICL3N1n2fRHZEp7/UDPbzwivs11E7mgyv7+ILBKRbeH3fh3zk7rO5gObOb7f8U6HYcJaui004vzzz++iaExP4/V7eXrV05x73LnMmTAHgNqGWsYNHMeA1AHR9SINwJEqop6sLSUAHzBdVScAE4EZIjJVRM4DLgPGq+qJwMOHbygibuAJYCYwFviGiETK33cA76jqSOCd8OceY1/NPnZW7uzxdwH0JrGGh2xq8ODB1vhrmvXvr/47JdUl/OzsnyEiqCqfVXzGxEETD/mbWla8jFFZo8hMznQu2A7SagLQkJrwR0/4pcBNwIOq6guvtz/G5lOA7aq6Q1UbgBcJJQ3C73PD03OBy4/2R3S12oZavjXvW0D36dbVfHFb6HnnnceYMWOOWH7KKdZVh4ntz6v/zLzN8zg191QuHH4hADsrd1JSXcJJA7940KvcW878LfOZmjfVqVA7VJvaAETELSKrgf3AIlVdCowCzhaRpSLynojEOhMOAYqafN4dngeQo6p7AMLvA5vZ9/UiskJEVpSWlsZapct9uudT3t7xNgAn557scDSmqchtoWeccQYJCaHHXESEM8880+r+TUyqyg/f+iEDUgew4JsLovMj9f7ZqV903XzH26GKihtOvaFrg+wkbXoQTFUDwEQRyQTmici48Lb9gKnAZOAlETleD+2YJVZZvF2D5qrqk8CTAJMmTeoWA+5GTvq/nP5Lewism4r1kJgxsZRUl1BRX8HjMx4np09OdP7Y7FBt9Y6KHUBoOMenPn2KmSNmckb+GY7E2tHa9SSwqh4UkcXADEJX86+GT/jLJDTqxgCg6WX6bqDp/7w8IHKLxj4RyVXVPSKSS6h00SP0SexDSkIK5d5yp0MxLcjPz7cTv2lVZBzv/L6H/q2kJKTgFnd0lL/TnzkdgO9N/l7XBtiJ2nIXUHb4yh8RSQEuADYDrwHTw/NHAYnA4Y/MLQdGisgwEUkErgJeDy97HZgTnp4DzD+WH9LVUj2pNv6vMb3AsMxhwJEPfNX6awlogD8s/wMPffgQ6/evp29SX2aMmOFEmJ2iLSWAXGBu+I4eF/CSqr4RPqE/KyLrgQZgjqqqiAwGnlbVWaraKCK3AG8BbuBZVd0Q/t4HCVUbXQvsAq7s4N/WqYZkDGHtvrXdsotXY0zb+QI+AAb1OfQBwuSEZNzipspXxU/f/imJ7kSKbisiwdV7ulBr9Zeo6lrgiJbO8F0934oxvwSY1eTzm8CbMdYrA3rsTdnfmfgdbn3rVt7Y+gZfHv1lp8MxxrTDs6ue5aYFN/HYxY9FL+BOHXzoTQIJrgQqflrB6c+czobSDTz15ad63YOfEmswje5q0qRJumLFCqfDAEKPjE/47wn4Aj42fG8DyQnJTodkjGmDsroyhjwyJHrlf9W4q3hx/YtU3VEV8wRfWV/J75b+jv88+z977NW/iKxU1SOeXLOuII6Sx+3h8ZmPs6NiBw99GPMhaGNMN7S0eCm+gI+fnPETAF5c/yLnHndus1f3fZP7cs+59/TYk39LLAEcgwuOv4Arx17Jgx88GB0cxnRvRUVFvP/++xQVFbW+sumVvvTClwC4dPSl0XnD+w13KhxHWQI4CoFggP21obtWb5p0E95GL8tLljsclWlNpLO4d999l+eee86SQJybMmQKP5/2cwC+e8p3HY7GGZYAjsLt/7ydnIdzeOyTxxjRfwQAhQcLnQ3KtCrSWZyqEggEKCwsdDok44ChfYcCoWrcO8++k70/2svp+ac7HJUzLAG0U21DLc+segaAu/51F/6gHwg1CpvurWlncW6324aFjFPnFZwHhAZ2T3AlHPL0b7yxBNBOC7YtoLqhmgfPf5Bafy3PrnoWgLTENIcjM61p2lnc7Nmz7SnhODVx0EQAe5ATGxS+3d4rfI/0xHR+dMaPmLtmLr98/5cADEyL2Zed6WasewjjFjcAe2v20i+lxw1D0qGsBNBOJTUlDO07lARXAtedch0AHpeHM/PPdDgyY0xbeNweAFbvXe1sIN2AlQDayev3Rh/6umHSDXx+8HPOOe4c+ibb2MDG9AQPffgQbnEzrWCa06E4zhJAO43sP5IlO5ewvXw7I/qP4PGZjzsdkjGmHfbW7OXrJ36d3PRcp0NxnFUBtdN/TP0PUj2pnDf3PD4r/8zpcIwx7dQnsU+vGM6xI1gCaKcR/Ufwzux38Pq9TJs7LfpAmDGm+6ttqKW6oZpaf63ToXQLlgCOwoRBE1h09SJ2V+3miWVPOB2OMaaNXlj3AvWN9Vw2+rLWV44DlgCO0sm5J5ORlMHB+oNOh2KMaaMnlocu2C4ZdYnDkXQP1gjcTlW+Kt79/F1y+uRQ5avihOwTnA7JGNMGQQ2yZt8axg0cR6I70elwugVLAO30zb99kwXbFgChxqRvnvRNhyMyxrRFcVUxADdPvtnhSLoPqwJqh3X71rFg2wKGpA8hwZXA4zMeJyMpw+mwjDFtsLVsKwCjskY5HEn3YSWAdli4fSEAn97wKRlJGTYKmDHd2N+3/J1LX7yU7NRs3vjmGzzyySMAjM4a7XBk3YclgHaoaagBIDs12waCN6YbCwQD3PrWrQCU1pVy2tOnAVCQWcCQjCEORta9tFoFJCLJIrJMRNaIyAYRuT88/z4RKRaR1eHXrBjbjm6yfLWIVInIrW3dvrup89eRkpBiJ39jujGv38v056azo2IHL371RT749gfRZa9c+YqDkXU/bSkB+IDpqlojIh7gAxH5R3jZo6r6cHMbquoWYCKAiLiBYmBek1Va3N5pDYEG1u5by7iB4/C4PNT560j1pDodljGmGe8Vvse0udMAmDliJleeeCUucfHJtZ9Q7i3n1MGnOhpfd9NqAlBVBWrCHz3hlx7Fvs4HPlPVnUexrSO+Pf/bvLDuBQDOyD8DQRiQOsDhqIwxzXllY+gK/5GLHuHGSTfiklAlx2l5pzkZVrfVpruARMQtIquB/cAiVV0aXnSLiKwVkWdFpLWOta8C/vewea1uLyLXi8gKEVlRWlralnA7xPby7dGTP8BHRR/xYdGH3DTppi6LwRjTPot3Luai4Rdx2+m3keJJcTqcbq9NCUBVA6o6EcgDpojIOOCPwHBCVTx7gN82t72IJAKXAi83md2m7VX1SVWdpKqTsrOz2xJuh3hq5VO4xMX2729n/U3rSU5IZtbIWXxv8ve6LAZjTNsVVxWzfv/66JCPpnXtugtIVQ+KyGJgRtO6exF5CnijhU1nAp+q6r4m3xWdbsP2XaraV81/r/xvvnLCVxjefzgA+2/fT5/EPtYAbEw30xBo4KEPH+Lud+8GsH5+2qEtdwFli0hmeDoFuADYLCJNO9O+Aljfwtd8g8Oqf9q5fZfaVr6NKl8VV514VXReelK6nfyN6YaeXfVs9OR/2pDTrHuWdmhLCSAXmBu+i8cFvKSqb4jIX0RkIqEG4ULgBgARGQw8raqzwp9TgQsjy5t4KNb23UFQgwAkJSQ5HIkxpjVvffYWeRl5/PNb/2Rk1kinw+lR2nIX0Frg5Bjzr25m/RJgVpPPdUBWW7fvDjyu0JihDYEGhyMxxrSmwlvBsMxhduV/FKwvoBgiV/51/jqHIzHGtKa6odru+DlKlgBiGJY5DI/Lw/r93aZZwhgTw9ayrazas4pJuZOcDqVHsgQQQ1JCEuNzxrO8ZLnToRhjWvDa5tdQlNkTZjsdSo9kCaAZkwdPZkXJCg7UHeDE/3cid7x9h9MhGWPCVu1ZxZMrn+QXS37B9GHTrYvno2QJoBlnDT2LKl8V357/bTaWbuShDx8i1CuGMcZJvkYfpzx5Cje8cQPVDdVcMvISu0X7KFkCaMblYy4nJy2HN7aGnk9TlFp/rcNRGWM+LPrwkM8XDb/IoUh6PhsPoBlpiWnMv2o+9y6+lzp/He/vet/GETWmG5i7Zi4Ay767jHJvOScOPNHhiHouKwG04LS801j4rYWcMOAE0jxp0ecDjDHO2Fq2lefWPAfA5CGTuXjExQ5H1LNZAmjFzoM7mbtmLleccIXVMxrjsJc2vATAkmuWOBxJ72AJoAWvbX6NEb8fgYjw6/N/7XQ4xsQtVWVj6UZ+v+z3nJ53Omcfd7bTIfUKlgCaUVJdwr+98m80Bhv51fRfkZeR53RIxsSt59c9z4n/70Qq6yt56stPOR1Or2GNwM34wT9+QIIrgTU3rmHMgDFOh2NMXHtt82sALL5msTX6diArAcRQ01DDa5tf43uTvmcnf2McVlZXxqIdi5g9YTZT86Y6HU6vYgkghp0HdxLQABMHTXQ6FGPiiqry6qZXKa39YvjX59c9T5Wvih+d/iMHI+udrAoohkh9f3F1scORGNP7BTXIvz7/F4FggN8t/R3/2P4PThtyGp989xMAlpcsZ0j6EE4aeJLDkfY+lgAOU+GtoKK+guzUbLaVbXM6HGN6rfX71/Pl//0yhQcLj1i2tHgpcr9w0sCTWLd/HRcPv9huw+4EVgXUxJvb3mT448MZ+fuRlNaVsnjnYgLBgNNhGdMrXfzXi6Mn/5SEFG6ZfAsD0wby6MWPRtdZt38dfZP62m3YnUR6UgdnkyZN0hUrVnTa94/5wxgaAg1U1FdwsP4gAF8b+zVevvLlTtunMfHovcL3mDZ3GrMnzObRix+lf0r/Q5YHNYhLXHj9XoIaJC0xzZlAewkRWamqRwyaYFVAYZsPbGZL2Rb+MPMPpCelM+e1OQC8svEVVNWKn8Z0kEAwwA1v3EBBZgFPzHqCPol9jljHJaHKCRvpq3NZFVDYvE3zALhszGV8Y9w3GJY5LLpsY+lGp8Iyptd5e8fbbCnbwq+m/yrmyd90nVYTgIgki8gyEVkjIhtE5P7w/PtEpFhEVodfs5rZvlBE1oXXWdFkfn8RWSQi28Lv/TruZ7XfvM3zmDx4MnkZeXjcHu4464sBYMq95Q5GZkzvUHiwkMtfvJwZz8+gf0p/vnLCV5wOKe61pQTgA6ar6gRgIjBDRCJPYzyqqhPDrzdb+I7zwus0rYO6A3hHVUcC74Q/O2J31W6WlyznijFXROfNmTAnOj2071AnwjKmV7nxjRuZv2U+6YnpvHzlyyQlJDkdUtxrNQFoSE34oyf86oiW48uAueHpucDlHfCdR2X+5vkAXHHCFwkgKSGJ+VfN55qJ11gCMOYY1TfW897O97h58s0U3lrI9GHTnQ7J0MY2ABFxi8hqYD+wSFWXhhfdIiJrReTZFqpwFPiniKwUkeubzM9R1T0A4feBR/cTjt28zfMYM2DMEd0+XDr6Uv7nsv+xBmBjjtEHuz6gvrGeWSNnHXHHj3FOmxKAqgZUdSKQB0wRkXHAH4HhhKqF9gC/bWbzM1X1FGAmcLOInNOeAEXkehFZISIrSktLW9+gncq95SwuXMzloy/v8O82xoSsLFkJwCm5pzgciWmqXXcBqepBYDEwQ1X3hRNDEHgKmNLMNiXh9/3AvCbr7RORXIDw+/5mtn9SVSep6qTs7Oz2hNsmC7YuIKCBQ6p/jDEd653P32F01mgG9RnkdCimibbcBZQtIpnh6RTgAmBz5OQddgWwPsa2aSKSHpkGLmqy3utApKV1DjD/KH/DMZm3eR5D0ocwafARz0gYYzrAox8/yqIdi5g8ZLLToZjDtOVBsFxgroi4CSWMl1T1DRH5i4hMJFTHXwjcACAig4GnVXUWkAPMC9ehJwAvqOrC8Pc+CLwkItcCu4ArO+xXtVGdv46F2xfynZO/E33wxBjTcV7Z+Ao//OcPAXjgvAccjsYcrtUEoKprgZNjzL+6mfVLgFnh6R3AhGbWKwPOb0+wHW3RZ4vwNnq5fMzlToZhTK915cuh67ol1yyhILPA2WDMEeL6snfe5nn0S+7Huced63QoxvQ6mw9sjk7bGL7dU9wmgMZgI3/f+ncuGXUJHrfH6XCM6XVOeOIEAB664CGHIzHNidsEsGTnEsq95Vb9Y0wnyUzOBODHZ/7Y2UBMs+I2ASzYuoDkhGQuHn6x06EY0+vUNNRQ7avmZ2f/zOlQTAviNgF8fvBzhvcbbv2MG9MJ7n33XgIa4MLjL3Q6FNOCuE0A+2v3MzDNsd4njOnV9tTsAeCsoWc5HIlpiSUAY0yHG58zHghVBZnuyxKAMabDnTTwJAA2lG5wOBLTkrgdEjLVk8q8zfPwB/ws/Cz0cPLmmzdbH+XGdICs1CwAKrwVDkdiWhK3JYAfnv5Ddlft5oX1L1B4sJDCg4XW7bMxHcTX6AOwC6puLm5LALefcTvfPeW7pCemM+P5GVR4K0h0JzodljG9wpvb3sQt7mhbgOme4rYEAKEHVdwuN+v2rbM/VGM6SE1DDX9e82e+NOpL1s7WzcV1AoBQUXVf7T6GZQ5zOhRjeoXPKz5nf+1+Zo6Y6XQophVxnwB2V+0GIL9vvsORGNM7DEgdAMDOgzsdjsS0Ju4TQFFVEQD5GZYAjOkIg/oMQhAag41Oh2JaEfcJoLiqGIAhGUMcjsSY3qHWX4uiVv/fA8R1AiirK+PVza8CMDh9sMPRGNM7RG4B3VW5y+FITGvi9jbQwoOFDPtdqOE3Jy2H9MR0hyMypnfISs3i5EEns7lsc+srG0fFbQlgyc4lAORl5PH+t9+3h8CM6SCqSp2/jqAGnQ7FtCJuSwAb9m8g0Z3I5//xOQmuuD0MxnS4j3d/zJayLfzkzJ84HYppRaslABFJFpFlIrJGRDaIyP3h+feJSLGIrA6/ZsXYNl9E3hWRTeFt/6PJsla370zeRi9J7iTc4u7K3RrTa3j9Xqp8VUfMn7dpHh6Xh6+N/ZoDUZn2aMulrw+Yrqo1IuIBPhCRf4SXPaqqD7ewbSPwI1X9VETSgZUiskhVN7Zx+04zZsAYqhuq2V21254BMKadVJWT/ngSn1V8Rt1/1pHiSQFge/l2nln1DDNGzCAjKcPhKE1rWi0BaEikU29P+KVt+XJV3aOqn4anq4FNQLe433Ly4MkAXPripfxpxZ8cjsaYnmVFyQo+q/gMgLd3vA3A1rKtfG/B9/AFfDx8kSPXdaad2tQILCJuEVkN7AcWqerS8KJbRGStiDwrIv1a+Y4C4GRgaZPZbd6+o506+FTGDRzH6r2ruXHBjby+5fWu3L0xPdpjSx+LTl8z/xryHslj9B9Gs2jHIm6efDOjskY5F5xpszYlAFUNqOpEIA+YIiLjgD8Cw4GJwB7gt81tLyJ9gL8Bt6pqpNKwTduLyPUiskJEVpSWlrYl3DZxiYuPvvMRJT8sISslyxKAMW20t2YvL294mR9M+QEA5d5yiquLo8tvP+N2p0Iz7dSu20BV9SCwGJihqvvCiSEIPAVMibVNuN3gb8Dzqvpqk+9q0/aq+qSqTlLVSdnZ2e0Jt1XpSenkpucyZcgUlhYvbX0DYwz3vnsvAN8/7fssuWYJXz3hq7x99dvcd+59NN7daE8A9yCtNgKLSDbgV9WDIpICXAD8l4jkquqe8GpXAOtjbCvAM8AmVX3ksGWtbt9VpgyZwsLtC6n2VZOeZA+EGdOc2oZanvz0SQb1GcSI/iMY0X8EZx93NgDnH3++w9GZ9mpLCSAXeFdE1gLLCbUBvAE8JCLrwvPPA24DEJHBIvJmeNszgauB6TFu94y5vRO2lW9D0WjHcMaY2Mq8ZQBce/K1DkdiOkKrJQBVXUuo8fbw+Vc3s34JMCs8/QEQ8xHb5rZ3wgvrXgC+6MbWGBNbbUMt8MWg76Zni9uuICLuePsOAKbmTbW6S2NaUesPJYBUT6rDkZiOEPcJ4J3P3wHg3nPvdTgSY7q/On8dAGmJaQ5HYjpC3HeCMyR9CKV9S7l4+MVOh2JMtxepArISQO8Q9yWAGSNmsLNyJxtKNzgdijHdXrQE4LESQG8Q9wngijFXkOZJ465/3YVqm3q4MCZuRdoArAqod4j7BJDTJ4d7zr2H+VvmM3/LfKfDMaZbi5QArAqod4j7BABw29TbSHAl8MnuT5wOxZhuLdIGYFVAvUNcJ4AdFTvYfGAzbpcbif24gjGmCSsB9C5xexfQrspdjP/jeBoCDTx96dP4g37GDRzndFjGdGu1/trQQEouG0ipN4jbBPDIx49EG7TmvDYHl7i48PgLHY7KmO6tzl9nV/+9SNxWAX28+2POH3Y+K65bwYj+I7jzrDvJ6ZPjdFjGdGu1DbV2B1AvErclgF2Vu/jSyC9x6uBT2fb9bU6HY0yPUOuvtRJALxKXJYBAMMDemr0MTh/sdCjG9Ci1/lq7A6gXicsE0BhsBCA5IdnhSIzpWawKqHeJywTgcXvISMpgd9Vup0Mxpkep9dfSJ7GP02GYDhKXCcAlLvom9cXb6HU6FGN6lJqGGqsC6kXiMgEAVPmq6OOxKxlj2sOqgHqXuEwAFd4KKn2VHJd5nNOhGNOjWCNw7xI3t4E+/NHDbC/fzp1n3cnOyp0AjM4a7XBUxvQsNQ011gbQi8RFAiirK+PHi34MwJ9W/olzjzsXj8vDOced43BkxvQcgWCA+sZ6KwH0Iq1WAYlIsogsE5E1IrJBRO4Pz79PRIpFZHX4NauZ7WeIyBYR2S4idzSZ319EFonItvB7v477WYfaWrYVCA3+AvDezvc4a+hZ9E3u21m7NKbXseEge5+2tAH4gOmqOgGYCMwQkanhZY+q6sTw683DNxQRN/AEMBMYC3xDRMaGF98BvKOqI4F3wp87xfby7QA8dvFjlP+knB9M+QG/Pv/XnbU7Y3ql6GAwVgLoNVqtAtLQMFk14Y+e8KutQ2dNAbar6g4AEXkRuAzYGH6fFl5vLrAY+Gkbv7ddPqv4DEEoyCwgKSGJ3838XWfspsdbXLiYB5Y8QKonlZSEFBLdidGXx+U55HOiO5GkhKQvpt1Jh8xP9aTicXlwiSv6crvceFwePO7Qd/VJ7EN6YjppiWm4JC7vR+hRahpCpwFrA+g92tQGEL6SXwmMAJ5Q1aUiMhO4RURmAyuAH6lqxWGbDgGKmnzeDZwWns5R1T0AqrpHRAYew+9oUX1jPS5x8fSnT5OelB498aR4UmKu7xIXbnGT4EqIjhUQ1GDMl6KoavTdJa7oiRBAw7kyMtykNsmdh89r+rnpdyqK1++lylcVPZkevk1L+4p8R4Rb3Lhd7tD3NNnPPYvvYf3+9YzPCXWT7Q/4aQg0HPHyB/3H8s9xBEFIT0onIynjkFd6YmheZnJm9NUvuR9ZqVn0T+lPVkroPTM507on7gLRwWCsCqjXaFMCUNUAMFFEMoF5IjIO+CPwAKHSwAPAb4HvHLZprFFW2jXwrohcD1wPMHTo0PZsGjWy/0gCGuCWf9xyVNvHk+SEZJZftzyawGJRVfzBUHLwNfoOSQ6+gA9fo486fx3+oB9VJahBAhogEAzgD/qjiaXWX0uVr+qIV3VDNVW+KnZX7aayvjI6rzmCkJSQFC2leNwePC4PblcoiSe4Eg5J6JFSSGQdt7gPKakc/ooky0NeuPC4PaQkpJDiSTnq9yR3EiItD0akqtT566j11+L1e6lvrMfb6I1O+4N+GoON+AP+6PFt+g58ceHQJOE3vYBpy/QHRR8AVgXUm7TrLiBVPSgii4EZqvpwZL6IPAW8EWOT3UB+k895QEl4ep+I5Iav/nOB/c3s80ngSYBJkyYd1ajt155yLd886ZtUN1RT01BDta+a6oZqvH5vzP98QQ3SGGwkEAzQGGxE0SNOEiISekcQkeh7UIPRk2FklLHIPg7/HGte089NvzvFk0J6Ynr0P2OsbVraV+S7VDV6Mg5q8Ij9ZKdlt3jyj3xnpJTTVdUBjcFGDtYfpMJbQbm3nDJvWei9LvTubfRGE4s/GDrxRf79Aho45N+z6cmxvrG+2dJdQAPNLov8O3v9XryN3mj/Uu0lCMkJydFXpFot8jdY5auisr6SgAY6+IgenZSEFIb3H+50GKaDtJoARCQb8IdP/inABcB/RU7e4dWuANbH2Hw5MFJEhgHFwFXAN8PLXgfmAA+G3zt1RPYUT+iKa2Bap9U0mU6U4EpgQOoABqQOcDqUmBqDjdFk0Nb3On8dXr8XX8BHfWN99NUQaIiWPDISQ1VgGUkZ9EnsQ3JC8iEliOSEZDwuT7Sk07QEFHmPXJgEgoFooo9cxLR3OrIf0zu05V8yF5gbbgdwAS+p6hsi8hcRmUioSqcQuAFARAYDT6vqLFVtFJFbgLcAN/Csqm4If++DwEsici2wC7iyA3+XMV0qwZVAelI66UnpTodiTJtJ00bE7m7SpEm6YsUKp8MwxpgeRURWquqkw+fbvXfGGBOnLAEYY0ycsgRgjDFxyhKAMcbEKUsAxhgTpywBGGNMnLIEYIwxcapHPQcgIqXAzi7Y1QDgQBfsp6ex49I8Ozax2XGJrauPy3Gqmn34zB6VALqKiKyI9dBEvLPj0jw7NrHZcYmtuxwXqwIyxpg4ZQnAGGPilCWA2J50OoBuyo5L8+zYxGbHJbZucVysDcAYY+KUlQCMMSZOWQJoQkQmiMjHIrJORP4uIhlNlt0pIttFZIuIXOxknF1NRCaKyCcislpEVojIlPB8j4jMDR+vTSJyp9OxdqXmjkt42fjw39KG8PFJdjLWrtTScQkvHyoiNSJyu1MxOqWF/0sXisjK8N/KShGZ3iUBqaq9NDrM4nLg3PD0d4AHwtNjgTVAEjAM+AxwOx1vFx6XfwIzw9OzgMXh6W8CL4anUwkNDFTgdLzd4LgkAGuBCeHPWfb3csjyvwEvA7c7HWt3OTbAycDg8PQ4oLgr4rESwKFGA0vC04uAr4anLyN0ovOp6ufAdmBKjO17KwUipaG+fDGuswJpIpIApAANQFXXh+eY5o7LRcBaVV0DoKplqt1kUN+u0dxxQUQuB3YAG47cLC7EPDaqukpVI8dpA5AsIkmdHYwN7nmo9cClhMYnvpIvBrQfAnzSZL3d4Xnx4lbgLRF5mFC14Rnh+a8QSo57CJUAblPVckcidMatxD4uowAVkbeAbEIXDw85E6IjbiXGcRGRNOCnwIVA3FX/hN1K7L+Zpr4KrFJVX2cHE3cJQETeBgbFWPQzQtU+j4vIPYQGrW+IbBZj/V51+1Qrx+V8Qif3v4nI14FngAsIlYICwGCgH/C+iLytqju6KOxOd5THJQE4C5gM1AHvhIfke6eLwu50R3lc7gceVdUakVj/pXqHozw2kW1PBP6LUCmy82MN1zmZw4jIKOCvqjol0ripqr8OL3sLuE9VP3Yyxq4iIpVApqqqhP7nVqpqhog8AXyiqn8Jr/cssFBVX3Iy3q7SwnG5CpihqteE17sbqFfV3zgYbpdp4bi8zxel6kwgCNyjqn9wKNQu19yxCS/LA/4FfFtVP+yKeKwNoAkRGRh+dwF3Af8dXvQ6cJWIJInIMGAksMyZKB1RApwbnp4ObAtP7wKmS0gaMBXY7EB8TmnuuLwFjBeR1HD7yLnARgfic0rM46KqZ6tqgaoWAI8Bv4qnk39YzGMjIpnAAuDOrjr5QxxWAbXiGyJyc3j6VeB/AFR1g4i8ROg/cSNwc5w16l0H/C58MqsHrg/Pf4LQMVpPqJrsf1R1rTMhOiLmcVHVChF5hNBdZQq8qaoLnAuzyzX392KaPza3ACOAu8MlRoCLVHV/ZwZjVUDGGBOnrArIGGPilCUAY4yJU5YAjDEmTlkCMMaYOGUJwBhj4pQlAGOMiVOWAIwxJk5ZAjDGmDj1/wH/KfeY7kBkHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the NYC-LI map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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h57UkpfEspgFngPq5r08CjwF3fpPlIoTQAl8Bg4Bo4G8hxB9SytNlM7fm4FzLmW3jtjH257G8uPlFkjKTeKf3OwXc1zxvQ6fTERYWZp56CggIKNRjyItzFDVFVVaklATHBBPQJqDcYykUlib1ZiqP//w4Oy/t5L0H32NWv1kVOg10I/sG8w/NZ3DrwfTw6FFh57U0JRILIYQHMByYA/wfgJTyTO57d+vaHbggpbyU23YtMApQYlECnOydWP/EeiZtmMS/dv+LxMxE5g2ad8c1LyymURi3r6rKi3OUdxrq6vWrxN6IpVuzIh1MhcImnIk/w9hfxnI24Szfj/qeSX6TKtyGxccWE58RX6W9Cii5Z7EQeAuoV8rxmwP513VGA/cX1lAIMRWYCuDlpbKY5mGvtWfVo6twqeXC54c/JzA6kPaN2vPZoM8KBOfyxzTyTzWVJM5RXi/jcuplwJQmXaGoDGTmZDJn/xzmHpxLXYe6bHtmGwNbDaxwOzJyMph3aB4PtXqIBzwfqPDzW5JixUIIMQKIk1IGCSH6lXL8wtyOQve4SymXAEsAunXrVrX2wVsZjdCwaOgiPOp78M6udzioO8iAJgPwyvEq1VRTfg8kNTWV4ODgIgPhpcHZ0RmA69nXy/9hFYpykJiRyC+nf2HeoXlcTL7I+HvHM3/w/ArbnX073xz7hrgbcVXeq4CSeRa9gJFCiGFALaC+EOIHKeW4EvSNBvJ/A3kAV0tvpkIIwdu936Z94/Y8uu5RDu08xEXjxTsEobANfLevhioszlGW/RV5NK/fHI3QEHotlLE+Y8v7URWKEmOURi4mXSQwOpCfT//MtgvbyDHm0NmtMzvH77SJN5FHRk4Gcw/OZUDLAfT26m0zOyxFsWIhpZwBzADI9SzeKKFQAPwNtBVCtASuAE8ChWfEU5SI+BvxANQy1EJypyDkn2q6mwiUNM5REpzsnajrUJfEjMQyj6FQFEa2IZur168SnRbNlbQrRKdFm55fNz0/FX+KtKw0AJrVa8Zr97/GM52fwa+pn833Mqw+vprYG7H8t9t/bWqHpSjzPgshxKPAfwBXYLMQIlRKOUQI0QxYKqUcJqXUCyH+AWwHtMAyKeUpi1heQ0nISACgnrYewnjn8tjSiMDtezfKSpY+i8ycTIuVn1QoDusO8+G+D9l+YTvytpnrOvZ18KjvQfP6zXm609N0a9YN/2b+dHbrXCFZY0tKt2bdaFCrAa9tfY1WDVuZyw5UVURlTJPbrVs3eezYMVubUSmZvm06S4OXcmb8mbsKQlFBbmvx7IZn+fHEjxx/8TjtG7e3+vlqAgajgfDEcBy0DrRxaWNrcyqEA1EH+OCvD9h5aSeNnRrzfJfnadeoHc3rNTcLhLOjs829hpJyKu4Uw34cRkJGAj8+9qNVMxwIIYKklFZbkqjEooox5IchxN+IJ/iF4CLbWGs/xd24ln6NDl914N4m97Jn4p4Ky7dTXcgx5HA6/jTBMcEExwQTFBNEWGwYGTmmxI73NL6HexrfQ9emXenq3hX/Zv40rdvUxlYXJDY9lk3nNrE7cjeNajfCx9WHTm6d8HHzoUGtBkX2MxgN/HX5L2bvm82eyD241XHjzQfe5KVuL1HHoU7FfQArcS39GiPXjOTY1WPMHzyf13u8bhWxs7ZYqHQfVYyTcSd5qNVD5teHD5tqVfTrdythYP4gt16vJywszOpi0bRuU+YPms/kjZNZGryUqf5TrXq+6sKfF//k3d3vcjz2OFmGLADqOdSji3sXpnadin8zfxIyEtgVsYsz8WfYcHaDeVrGva47/s388Xf3N++e1xv16I16DEaD+bneqMcgb73O0mcRdyOO2BuxONdypk3DNrRxMf3d0/ge6jmWbIV8Rk4G5xPPs/n8Zjae28iR6CNIJE3rNiU9O5307HRz22b1mt0SD1cfPOp7EBQTxP6o/RyMOsj17Os0rduUBYMX8EK3F+6oCFmVaVq3KXsn7WXC+gn8c8c/ORx9mO9HfU9dh7q2Nq1UKM+iCpGUmUSjuY2Y+9Bc3uz1prkaXna2qWbFwoWQmAh+fjqCglaYs8tqtVomTpxodcGQUjJg5QBCYkI488oZ3Ou5W/V81YE3d7zJ/MPz+b8e/2eee2/j0qZIz+x61nVCr4USFBNk9kDOJpzFKI0lPqdA0NipMW513Ei5mcKV61cKvN/CuQWd3DrR2a0z7Ru350b2Da5cv8LV61dvPaZdITXrVj2Ubs26MbLdSB5u/zC+TXyRSHSpOk7GneRU/CnTX9wpTsefJlOfae7n4+pDH68+PNjiQR655xFq29cu5RWsOhilkXG/jWPNyTX8Of7PAj/6LIHyLBRmTsSeAKBzk85AwWp4WVnwj3+A0QgODp4sWuTHlStBwK3a2tYWCyEES0YsofPizry69VV+GfuLVc9XHciLRUzrMQ0v5+I3o9ZzrEefFn3o06KP+diN7BtEpUah1Wix09ihFabHvL+843nvaTXaAmKUkZNBRHIE55POczr+NCfjTnIy7iQ7Lu4gx5gDgFZoca/nTvN6zWnfqD0DvAfQvH5zPOt70r9lf5rVa1bAToGgRYMWtGjQguHthpuPG4wGIlMiiUqNonOTzhWa9dXWaISGf/X5F2tOruFswlmzWEgpq0QMRolFFeJEnEksVn/RmQ1G6NLF5FFkmWYv0OtBSpOAXLvmjkajQUpZ7n0UpaFto7a83/d9/rX7X/x+9nceueeRCjnv7Ugpic+Ix0HrQC27WjhqHSvlf8i2jdoCcD7xfInEojDqONQxV1csC072Tvi4+eDj5lPg3yvHkENkSiT1HOvhVsfNInEorUZLa5fWtHapmbv9O7l1wq+pH9+FfMcr973C2zvfZt6heYT/I7zSJ+JUYlGF2HniBGQ25IevTb/i7OzggQfg4EGTRyElaDTg7a1Dym0YjUY0Gg0BAQEVsiIqjzceeIO1p9byypZX6O/dH+dazhV27jxm75vNe3vfK3DMUetIR9eO7Bi/o9L8os37gjifdN6mG8gKw15rbxYzheWY2nUqL295mQeXP8iBqAMAhF4LrfRioZasVCFCrpyE2M7kZVHR62HfPtM0lJQgBDz0EHzwQSRSmuIVUkpOnz5daOlVa2Gvtefbh78l5noMM3bNqLDzFsbs/rP5ZOAnvN/3ff7R/R+cjj/NM789g8FYeLXAiqZZvWbUtqvN+cTztjZFUUGMu3ccdR3qEhgdyJwBc2herzkf/PUBOYYcW5t2V5RnUUWQUpKoPYkmcRxFhTKlhNGj4cEHvYmI0JhXRF26dImoqKgKWUKbR/fm3Zl2/zQWHlnIM52fsXihmYycDBy1jkVuwrqn8T2AqYqfX1M/8/F2jdrxwqYXmLN/Du/1fa/QvhWJRmho49KGc0nnbG2KooKo51iPbc9so45DHfya+tHWpS1jfxnL72d/Z4zPGFubVyTKs6giRKVGcUOfxj/HdeaRR0xexO1oNBASAv/9bz2iopoXeC8vLUhF8tGAj2jh3IIpG6eQpc+yyJg5hhw++usjnD915p6v7uG74O/INmTf0S5vY2B4QniB41O6TmH8veOZtXcW+y/vt4hN5aVto7bKs6hh9PLqZf4R41HfA6DSZ0BQYlFFOBl3EoBRPTrTvfudYiGE6e+772D+/Pp8//04dDoP8/sajabCgtx51HWoy+LhizmTcIYvAr8o93jnEs/xwLIHeG/vezzc7mHqOdRj8sbJtF7Umi8Dv+RG9g1z27YubREIziacLTCGEILFwxfj6ezJtG3TSrXk1Fq0dWnLpeRL6I16W5uisAGB0YEAlb4wkhKLKkJ4oukX8tJPO7Bt253vS2mKXeTkSIxGDQaDhshIb/P7fn5+FRrkzmNo26G0bNCSsNiwco817rdxXEq+xE+P/8RvT/xG0NQgtj6zlZYNWjJ9+3S8v/Rmzr45pNxMobZ9bVo0aGG+bvmp41CHTwd+Ssi1EL4P+b7cdpWXdo3akWPMISo1ytamKGxAys0UAC4mXbStIcWgxKKKsP/0OchoxPLFLuzbZ1r9VDRGvLyi6Nz5OAB2dna4u7uzf//+Cg1055GalUqj2o3KN8bNVI5dPcZr3V8zz+sKIQhoE8C+Z/exb9I+7mt2HzP3zMR7obd5dcn5pMKnd57s9CR9vPrwxp9vcPW6bbPmt3W5tXxWUfN4+b6XaevSluE/DickJsTW5hSJEosqwvEr5yDx7kvrhAA7O8mgQbuZOHEVDRqYUjc3b96crVu3smfPHlauXFmhgmGURpIzk3Gp7VKucQ7qDiKRPNjiwULf79OiD1ue2ULQ1CAc7RyZunEqXvW9iEyJLLS9EILvRn7HTf1NXtz0IrbMZJC3PPVcogpy10Sa1G3Czgk7qW1fm4m/T7S1OUWixKKKkCTuLhYaTd6joF+/a2g0t778Ll++fEdBpIoi5WYKElluz2Lf5X3Ya+y536PQqrxmurp3ZcHgBfx99W92RuwkISOhQI6i/LRt1JbZ/Wez8dxGpm6cyqk422TPr21XG3uNfZFekKL6417XnRxDDh1dO9ralCJRS2erAOnZ6aQYryCS2hVak1arNcUsTLu3JXv2uNG7d+HznxW5mxswF0Qqr2ex7/I+7mt+X4kSzD3d+Wlm759tDm5fTrmMj5tPoW2n95jO+aTzLA9dztKQpfi4+uBcy9m869vRzhFHraP59X3N7+O5Ls9ZZDezlJLfzvzGa9teQ2/U09mtc7nHVFRNVoatJDEzkXH3lrSuXMWjPIsqwIWkC6YnRXgWvXqZdnObypsL9uwZQFJSQ+rVq4dGo0EIU5Ekf39/AgICiIyMrLCpqKTMJAAaOZXdswi6GsSxq8fo49Wn+MaYppga1GpAfcf6AEVORYEp/cQ3I74h+v+imTdoHi0btsTJ3olsQzYJGQlcSr5EyLUQ9kbu5bezvzFl4xR6L+vN6fjTZf48ALpUHaPWjuLxnx/H1cmVI5OPMMV/SrnGVFQ9sg3Z9F3el8kbJ9OtWTeGtx1efCcboTyLKsCmw6a5bJlQuFjcvAk9eph2c4PA3f0qzs6ppKebEpS5u7vTtWtX3NzcKrzORWKmybMoyzTUmfgz/HvPv/n1zK+41Hbhmc7PlLivncYOz/qenIo/RURKRLHtGzs15o0H3uCNB94oso2Ukh+O/8D07dPx+8aPd/u8yzu938HRzrHEdhmMBv5z9D/M3D0TiWTeoHlM7zEdO436r1gTCYwOZN/lfbzg/wIf9PugUuYvy0PdoVWAPSdPgxSQVHi1tL//Nk1BmdKASHx9jyOEESnBaJRcvXqVuLg4fH1974hdWF0syjgN9fvZ33nylyex19rzft/3eb3H66XKMaUVWlxqu1DLrpbFAsdCCMb7jmdImyG8vv11Zv01i59O/8TSh5fS07Nnsf1DYkKYsnEKQTFBDG0zlK+Hf413A2+L2Kaomqw7uQ6BYM6AOeXyvisCNQ1VBchuFAIJ90BO4fP1+RfyeHhE4+ERjcFgh9EoMBo1JCU1MNe20ORGwitqk15ZpqGWhy5n9E+j8Wvqx8XXLjKr36xSJyPUarTsj9rPTf1NOrl1KlXf4nCr48bqx1az5ektpGen02tZL17d8irXs64X2j49O51/bv8n3b7tRnRaNGtHr2Xz05uVUNRwwhPCWXxsMS/4v1DphQKUWFQJzqUFo4ktWbF3X98wzp9vy7ZtQ5BSIIQRZ+c0hBCkp6cXWCIaFxdn9b0XiZmJCATOjnd+2V/Pus5nBz7D9xtffjvzGwALDi/g2Q3PMrDlQHZO2IlbHbcynfd4rGmPyfNdnmdKV+vEAoa2Hcqpl0/x2v2v8dXfX9Hx646EXSu4+XDzuc34fO3DgsAFTO4ymTOvnOGJTk9U6ukGRcXw1s63EELw6v2v2tqUEqGmoSo5cTfiiMuKhitFi4VGA61bw/nclZfe3pFcuNAGISQaDUhp5MaN2oSH39rNbDAY2Lx5M4BV4xdJmUk0rN2wQMK/lJsp/OfIf1h4ZCFJmUk0qdOEx396nMZOjYnPiGdMxzGsenRVqWIBt/NGzzc4EXeCxcMXW/WLua5DXRYGLOSpTk8x+qfRjFo7ikPPH0IgmLZtGj+f/pmOrh3Z/+x+env1tpodiqrFybiT/BH+B7P7z67Uy2Xzo8SikmPe0RlTtFgYjXAxd6VsWJgvkyatwN7elLgvz5FwcLh5R788L8Oa8YvEzMQC8YrVx1fzypZXSM1K5eF2DzPzwZl0cutE1/91JTwxnBf8X+CrYV8VmU22pLzd++3yml4q7ve4n/VPrOfB5Q/SZlEb7LX2ZOmzmN1/Nm/2ehMHrUOF2qOo3OStcBzadqiNLSk5SiwqOcExwaYn1/zu2i4v/Ud0tCfLl0/A2/syyclXuOeecDQaCmzSux1r7r1IzEikUe1G6I163v7zbRYELqCPVx++DPiSLu5dzO3+eOoPDukOMdF3YpWdormv+X0cf/E4nxz4hPTsdOYMmKOKBykKJe/Hw3t73uP3J3+vEqvhSmyhEEILHAOuSClHCCFcgHWANxAJjJVSJhfSbxowBdNSnW+llAvLb3bNIfhaMM1rtyZW34CS5iSNjvYiOtoTL68o7rnnHCALTWkuhKBr1674+vpabVVUUmYS9lp7An4IYFfELl7t/iqfD/78jnTM7Rq1q/SVwkpC20ZtWTZqma3NUFRy2jcypdDffH4z5xLPVYmpqNIEuKcBZ/K9fgfYJaVsC+zKfV0AIUQnTELRHfAFRggh1E+tUhAcE0xT2RXDXQq73SkEEiGMdO58vEiPQqPR8MADD+DsbN2Sp4mZiQRGB3Ig6gDfj/qeRUMXVfq8/QqFNbmSdoVH1z2KncaOlY+srBJCASX0LIQQHsBwYA7wf7mHRwH9cp+vAPYCt08UdwACpZQZueP8BTwKzC2P0TWFlJspXEq+hGbPZArLc9emDbi4wNGjt78jcXZOwc/vOAaDKQCeJygajYYuXbrg7u7Otm3brL5BL0ufRfN6zVn/xHrua36fxcdXKKoSEckR9F3el5SbKWx5eguDWg+ytUklpqTTUAuBt4B6+Y41kVLGAEgpY4QQha1xPAnMEUI0AjKBYZimsu5ACDEVmArg5eVVQrOqN6HXQgGQVwsPbl+4UFRPDSkpjViyZDLt25+je/cs+vc3Bbh9fX0B2Lt3L3q9aWLLmgHuHeN34F7XvUqsI1corE3otVB0aTp6efYqUO63KlDsNJQQYgQQJ6UMKu3gUsozwGfAn8A2IAwKn3qXUi6RUnaTUnZzdXUt7amqJXnBbcekLoXGHO6OJD7ejcOHe5KeHo27uzvOzs7ExcWxcuVKIiJupcCwZoC7k1snJRQKRS6PdniU/434H0euHMFtvhuPrXvM1iaVmJJ4Fr2AkUKIYUAtoL4Q4gcgVgjhnutVuANxhXWWUn4HfAcghPgYiLaM6dWf4JhgPOp78O+P3XjpJQqdirob7dufZcCAPTRpEs/mzaYqbEIIpJTmZbPNmjUjICDAJlX0FIqayFT/qfT26o3P1z5sPLfR1uaUmGI9CynlDCmlh5TSG3gS2C2lHAf8AeRV6pgIbCisf970lBDCC3gMWGMBu6s9UkoORx/G392fxMQ7K+PZ28OLL8Jbb4FTIVlAXF3jGDPmV9zc4s3jSSkx3jZQbGystT6CQqEogo6uHXm719vojXpz/rTKTnnSfXwKDBJCnAcG5b5GCNFMCLElX7tfhRCngY3AK4Utr1XcybnEc1xKvoS8MIS9e+9838cHrl2DwEDIyLjz/fj4JoSE+N3hjdjZ2dGu3a0lqkajsUKLISkUChN9W/QFYO7BqrHep1Q7QaSUezGtekJKmQgMLKTNVUyB7LzXJStCoCjAf//cBMDGz4cjU+58PzTU9Hc3wsJ88fMLQwgDdnYa/Pz8zAHuixcvmldCVWQxJIVCYWJo26E81ekpvvr7K97s9SaNnRrb2qS7ImxZe7gounXrJo8dK3TRVI2h1Yf9ibiWBIvDim98Fzw8dLRsGcmMGd4MHXorLqHT6YiMjMTb21vFKxQKG3E6/jQ+X/uwKGBRuRMKCiGCpJTdLGTaHVT+PeY1kJSbKUSxH+2Ft7nLXrwSER3tSXS0Jz4+MDRfGhpPT08lEgqFjenQuAMaoeFa+jVbm1IsKkV5JWT7he0YpAFxYUQZlswWzrXKfy8qFDUKvVHP+3vfxyiNVSLRpPIsKiGbzm+ilrExNy93N5XVVigU1YqLSRcZt34cgdGBTPKbdNdyvpUFJRaVDIPRwB9ntpB9ajgYy5emW6FQVC6klKw6vopXtryCVmhZ9/g6xvqMtbVZJUJNQ1UyAqMDSctJQp4bYdFxt26Fw4ctOqRCoSgFcTfieOynx5j4+0S6NO3C8ZeOVxmhACUWlY7N5zejFXZoIwZbdNycHArdr6FQKKzPr6d/xedrH7ae38r8QfPZM3EPXs5VKweemoaqZGw6t4k29n0IT29g0XGFgH79LDqkQqEohqTMJF7d+io/nvgRf3d/Vj5adVKS344Si0rE5ZTLnIg7gdjxucXHrqLF5xSKKsuW81uY/Mdk4jPi+bDfh7zT+50qXctFTUNVIracN2VJkeGWjVeAKQmhmoZSKKxPWlYaU/6YwvAfh9PIqRFHJx/l333/XaWFApRnUanYH7WfusbmpCdatryoEODgoKahFAprsydiD89ueBZdmo53er3DrH6zcLRztLVZFkGJRSXir4uB3Djb0+LjenjomD49Eg8Pb0Dt2lYoLE1GTgYzds5g0dFFtHVpy4FnD9DT0/L/l22JmoaqJMSmx3I1MwKp62HRcT08dEyYsJK0tD2sXLkSnU5n0fEViprOYd1h/L7xY9HRRbza/VVCXgipdkIBSiwqDUeuHDE9ibacWNSpc52WLSPQag0IIdHrDSoduUJhIbL0WczYOYPe3/cmy5DFrgm7WDR0EXUc6tjaNKugpqEqCYHRgQhph4wpvN526ZHUqpVJZKQ3BoMWKQ1oNFpSU1PR6XQqiaBCUQ5CYkKY8PsETsad5Pkuz7NgyALqO9a3tVlWRXkWlYTtpwKRV/1AX9tiYyYmNkan82TlyvHs29eXhITGBAcHq+kohaKMJGcm8+aON+m+tDuJGYlsemoTS0curfZCAcqzqBQYjAZOJP4N0ZMsOq6razzt24eTk2NPmzYXcHW9hpRgMJimo5R3oVCUjGxDNl///TUf7fuI5MxkJvlNYv7g+bjUdrG1aRWGEotKwL7L+8jRpMPlBy02ZpMm15g8eRlarakihkYj0Wg0SClVdTyFooRIKfn97O+8+eebXEy+yEOtHmL+oPn4NvW1tWkVjhKLSsAXu36ErLpwfrjFxuzSJRQ7O32Bnds+Pj64urqq6ngKRQm4kHSBV7e+yrYL2/Bx9WHrM1sZ0noIooamQ1AxCxuTpc9iu+4XOPso5DhZbNyzZzvdcVOfPHlSCYVCUQyZOZm8v+d9On3diYNRB1kweAGhL4YS0CagxgoFKM/C5mw5v4VsTQocf8ai42ZkeFKrlj83b96qZS6lVLEKheIubDq3ide2vkZESgRPdXqK+YPn06xeM1ubVSlQnoWN+fHkj2hvukHEQIuOGxcH7703lOjoW8KgYhUKReFEpkQyau0oHl7zMLXsarF7wm5+HP2jEop8KM/ChqTeTOWPsxuRJ6bkVsWTgOXc3JwcDS4uj+HvfwAAX19TUG7//v1qOkqhwDQNPP/QfObsn4NGaPjsoc+Y3mN6laiJXdGUWCyEEFrgGHBFSjlCCOECrAO8gUhgrJQyuZB+rwOTMX0TngCelVLeLL/pVZ/fzvxGtjELQp/GJBKS0gqGvT28/jps3Ajh4WA03nrPwQEeeaQBPXuastjqdDpWrlyJwWBAq9UyYcIEJRiKGomUkk3nNvHPHf/kfNJ5Hu/4OAsGL8DTWf1/KIrSTENNA87ke/0OsEtK2RbYlfu6AEKI5sBrQDcpZSdACzxZdnOrF8uOLaO+3g2udsckEnBLNO6ORmPKJisEtG4N588XFIru3WHPHuiZL0VNZGQkBoMBKaV5r4VCUdM4HnucQasGMXLtSDRCw7ZntvHzmJ+VUBRDicRCCOEBDAeW5js8CliR+3wF8EgR3e2A2kIIO8AJuFomS6sZMddjOHT1EH529zBwwC6aNLmGRmPklmdRtGA88ohJJEwb7ODXXwsKhb09LFxYUCgAvL290Wq1CCFU/EJR44i7EccLG1+gy/+6EBwTzKKARZx46QRD2gyxtWlVgpJOQy0E3gLq5TvWREoZAyCljBFCuN3eSUp5RQgxH4gCMoEdUsod5TO5erDu1DqMGOlMZ1z7HKZz51NcuNCGLVuG5X7xF67jb71lEovt2yE72zTVNHo07N8PWVkmj+P1128VOvLw0BEZGWmOUUyYMKHAa4WiupOlz+LLI18yZ/8cMnIyeLX7q7zX970atfvaEhQrFkKIEUCclDJICNGvNIMLIRpi8kBaAinAz0KIcVLKHwppOxWYCuDlVbUKmZeFbwO/xR13XHEFoEGDNPz9g3F1jSMiohVhYfeSnNyQPNFo0wbefBOmTjX1X7jQ5FGMHm061rmzSSAaNYLp001C4u2tY+LElUh5K0ahUNQUpJT8duY33tr5FpeSLzGi3QjmD5pP+8btbW1alaQknkUvYKQQYhhQC6gvhPgBiBVCuOd6Fe5AXCF9HwIipJTxAEKI34AHgDvEQkq5BFgC0K1bt+In7asw5xLPcTr1NIMZfMd7DRsmk5SUwsiRG9m1awDR0Z5oNKKAUBw+fEsQ9u83CUUeISGm4+7uOh54YC9GowEwxSjCwsIICwtTAW5FtSc4JpjXt7/Ovsv76OTWiR3jdjCo9SBbm1WlKVYspJQzgBkAuZ7FG1LKcUKIecBE4NPcxw2FdI8CegghnDBNQw3EtKKqRvPjiR8RCDrRyXzM2dmZq1ezqFfvBl26HMdgELRqFUFWlgMJCU2ZPt0kCj17mjyI7GxTvCI7G1auhBUrTM+1WvD3D2LIkC0IYQpk5MUogDsC3EosFNWJ6LRoZu6eycqwlTRyasTi4YuZ3HUydhq1S6C8lOcKfgr8JIR4HpMojAEQQjQDlkoph0kpjwghfgGCAT0QQq73UFORUrL6xGr6t+zP0z5Pc/r0aTp27IibmxvLli1HSlPgGqBDh1MYjRri45ty86ZJFHr2NNXSdnC4FbOAW+LRvLmOgACTUORlJmjZsiX9cgtw5/csVIBbUV1Iy0rjswOfsSBwAUZp5J89/8nMB2fiXMvZ1qZVG0olFlLKvcDe3OeJmDyF29tcBYble/0+8H55jKxO/H31by4kXWBG7xn4d/HH39/f/J6ra1vi4sIRwpQl1s0tnt9/bwNIpBQsWQJdupimo3btMnkYuRrAihWmALeXVyQgzUKh0WjMQhEZGUlAQAAZGRkqwK2oFuQYcvg2+Ftm7Z1FfEY8T3d+mjkD5uDdwNvWplU7lG9Wwfx44kcctA481uGxO97z8qpLfPyt11otuLnFExvbFDAtj33hBVNcYsIEmDHjVttdu2DWLDhzxlQZDwwIIWjbti1xcXFs27ZNxSoU1QYpJX+E/8HbO98mPDGcvi36Mn/wfLo162Zr06otKjdUBaI36ll7ci0j2o2gQa0Gd7zv6+uLRmP6J8nzDHJy7tTzb76BgQNNge48evY0rYzS6TxZsWI8u3f359y5toSHh7N582b0er3ajKeoFvx95W/6rejHI+seAWDDkxvYM3GPEgoro8SiAtkdsZvYG7E807nwDLOenp506dLltqOFp/7IzDTFMPKTmGiavoqO9uLgwV5kZjoCpl9heahYhaKqEpkSyTO/PUP3pd05E3+Gr4d9zYmXTjCy/cganTq8olDTUBXIjyd+xNnRmWFthxXZxtfXl5CQEIy5W7Lr1k0HwNU1lsTExhiNGvIEZOlSuHYNmjY1TUv16wf29sbcVVFGvL0jC4zdqlUr+vXrp6agFFWKlJspfLz/Y7488iUaoeHdPu/yVq+3akTd68qEEosKIjMnk9/O/MaYjmOoZVeryHaenp4MGzaMzZs3I6XE1/c4sbGutG4dwV9/9eVWKhCBXg+//27q97//mTbtrV2bwNdfn8bL6xINGqQVGLtjx45KKBRVhmxDNov/XsyH+z4kOTOZCb4TmD1gNh71PWxtWo1EiUUFsencJq5nX+eZe4svcuTv74+bmxs7d+4EoujT5wCOjtkcONAbvV5S2OyhlDB3Lvzvf0345JNoNm++grxta2NGRoZlPoxCYUXibsSx9uRaFh1ZZK57PW/QPPya+tnatBqNillUEKtPrMa9rjt9W/QtUXtPT0+effZZOnfuTNOmsXh56Zg8+Vu6dg3N10rSokUEHh5RgGna6rvvTKIgb1MKjUajYhWKSo2Ukm+DvqX1otZM2zaNBrUasPWZrewYt0MJRSVAeRYVQHJmMlvOb+HV7q+i1WhL1fe+++7jxIkTSAlNm8bj5xdKWJgvBoMWOzs9AwbsoVmzGFasGI+T0w3c3C4SHl7njnG6dOmipqAUlZbotGimbJzCtgvb6O/dn0VDF9HJrVPxHRUVhhKLCiAwOpAcYw6j7hlV6r55y1zzUpJ7eUUzceIKrl51p2PH09SvfwODAby9LxMf3wh//yCuXCk4hkajMVfJUygqE6k3U5l3aB5fBH4BwH+H/peX7nsJjVCTHpUNJRYVQFRqFACtG7Yudd/8U0d5guHpGY2nZzRgeq3RQEZGberWvWFuo9GI3D6CYcOGKa9CUanI0mfxzbFv+GjfRyRmJvJkpyf5eMDHtGzY0tamKYpAiUUFcDn1MnYaO5rWbVrusQpbTi4l3HvvcRo0SDUfa9++Pc2aNVNpPRSVCqM0subEGmbumUlkSiQDWw7ks4c+w7+Zf/GdFTZFiUUFcDn1Mp71PUsdrwAK3W19+yongBYtdGavQgh44IEHlEgoKhU7Lu7g7Z1vE3otFL+mfmwft53Bre9M06+onKiJwQrgcsplvJzLVtDJ29sbO7uCmi4EGAzCXEo1rxZ33nOAuLjCyosoFBVP0NUgBq0axJAfhpByM4UfHv2BoKlBSiiqGEosKoCo1ChaNGhRpr55pVAHDBhA48aNzcft7CRabeHTUgDBwcFlOp9CYSmSMpOY/Mdkun3bjZCYEBYOWcjZV87yzL3PqAB2FURNQ1mZHEMOV65fwat+2UvFenp64unpiZOTE5s2bSpRH3t7+zKfT6EoD3k1W/5v+/+RlJnEGz3fULUlqgFKLKzMletXMEpjmT2L/CQnJ5e4bXR0NDqdTsUtFBXKhaQLvLT5JXZe2sn9ze/nz/F/4ttULduuDihf0MrkLZtt4Vw+sdDpdBw6dKjE7VUqckVFkm3IZs6+OXT6uhNHrxzlq2FfcfC5g0ooqhHKs7AyeyP3AtC2Udsyj6HT6di2bdsdKTyKIysr665jRkZG4uTkpCrnKcrFgagDvLDpBU7Hn2ZMxzEsDFhIs3rNbG2WwsIosbAiaVlpLAxcyMj2I0td5jH/l/nWrVsxGAyFtjMa4cKF1tSunWXeqJdHREREkWOvXLkSvV5vPiaEoF27dvTq1QswLdlVAqK4G8mZybyz8x2WBC/By9mLjU9tZES7EbY2S2EllFhYka+OfkXyzWT+/eC/S9UvKCiILVu2YDQaEUKYPYr8jkX+VVA6nRdRUZ48/vgv1Kt3K7NsUQITFhZWQChMY0vCw8MJDw83F5JRJVgVRfHbmd94efPLJGQk8M+e/2RWv1nUdahra7MUVkSJhZVIz05nQeAChrUdVqpyj0FBQQVWPBUmFLfeM4mGg0M248b9iJ1dQQGIjY0lKCgIf39/89jBwcHExMTc1Ya8c+bFPZRYKPKIuxHHP7b8g59P/0yXpl3Y+sxWurjfXt1RUR1RYmElvjn2DQkZCaXyKnQ63V2XxgoBRqNASgEYzTu23d1j0GoNhe652L17N25uboSHh3Pw4MFSfQYhhEprrgBMPyDWnFzDa1tf43r2deYMmMObD7yJvVYt0a4pKLGwAhk5Gcw7NI9BrQbRw6NHifsV92VuNILBoGXr1gDq1LmBi0sivr4nMBiKrj+ckZHB8uXLzWVaS4Orq2up+yiqH1evX+WlzS/xR/gfdG/enWUjl+Hj5mNrsxQVjBILK7AkaAlxN+J4r+97Je6j0+kIDw+/47jRKNizpx/Ozqm0bn2R9esfJSrKC5C4uCRy8GAvevY8jEZT9Eqp4oRCSjh7th1162YUCJLHxsayYsUKJk6cqKaiaihbz29l3Ppxph9Ag+bxeo/Xy5TjTFH1KbFYCCG0wDHgipRyhBDCBVgHeAORwFgpZfJtfdrntsmjFfCelHJh+cyuvGTmZPLZwc/o792f3l69S9wvLCyswOu8eERerGLz5uG4usaSkeGEqQa3hqQkU/qPLVuG4+aWcMdqqOLIG9tg0HLwYC+uXm1G164h+PoeN49lMBjYu3cv/fr1K1Qw8lZtqZVT1Qu9Uc/7e97n4wMfc2+Te/np8Z9o37i9rc1S2JDSeBbTgDNA/dzX7wC7pJSfCiHeyX39dv4OUspwwA/MYnMFWF9Omys134V8x7X0a6wZvaZU/RISEu4QCClBSg0RES2REuLibk9xbpp+Mhg0REZ6l1gs8sY3GgXh4e04dKgX0dGegOTYMX9CQ/2YOHGlebxLly4RFRVlXhmVf1nvtm3bMBgMauVUNeJa+jWe+vUp9kbu5Tm/5/jvsP9S2762rc1S2JgSiYUQwgMYDswB/i/38CigX+7zFcBebhOL2xgIXJRSXi6LoVWFL498SS/PXiWutQ2mVUqXL5suS55g5D0PDb2XK1ea5YqIwORV5MUoTN/6Wq0Rb+/IEp9Pp/MgOLgrV664Ex/fpMBYoMFgkHeIT/4d4StXrjQvy82/ciosLEx5GVWcvyL/4slfnyT1Zirfj/qeSX6TbG2SopJQUs9iIfAWUC/fsSZSyhgAKWWMEMKtmDGeBIr8uS2EmApMBfDyKnvSPVuTkJHAsDbDzHsVikOn07Fly5YCXsWtX/4arlxxp16966SlNcjtkX9cgb19FlOn/krTpjEUsa0CuDVuUFBXtm4dhtGoyR1L5nsEN7cY7rnnHG3bFoyfaLVanJyc2Lt3LwaD4Y7d5EIIQkNDMRqNysuoghilkc8OfMbMPTNp49KGHeN20LlJZ1ubpahEFCsWQogRQJyUMkgI0a8sJxFCOAAjgRlFtZFSLgGWAHTr1q10eS2qMJGRkRiNRrPXkCcYFy+2Yu/evkRH3104mzaNo1GjS+j1JqUoSqN0Og82bx5GXFyT3HPlNcz/KPH0vEL//nvv6N+8efO77iRv2rQpMTExSCnV/owqRlJmEhPWT2Dz+c2M9RnLtw9/S33H+sV3VNQoSpJIsBcwUggRCawFBgghfgBihRDuALmPd6u2MxQIllLGltPeakfePoaUFGeMRg1Go0CvtyuRUAA0bJjIgQM9iY72AExCk5TUgKSkBiQmNjB7KYmJLsTGNkHKPI+icEJD/cxj5efy5csFhKJZs2ZoNKbbR6vV0rVrV7RaLUIItFqt2p9RRTh65Shd/9eVHRd38J+h/2Ht6LVKKBSFUqxnIaWcQa5HkOtZvCGlHCeEmAdMBD7Nfdxwl2Ge4i5TUDWZvIp2N27UZf36R2jRIorIyBYlEgqA48fvBUCjMdK06TXq1EnnwoV2SAl2dgYmTlyJh0c03t6X0WqN+fZkFCYYAqNREBFhilfkj5/kx87Ojq5du7J161bzMTc3NyZMmKBiFlUEg9HA3INzeW/vezSv15wDzx2ge/PutjZLUYkpzz6LT4GfhBDPA1HAGAAhRDNgqZRyWO5rJ2AQ8EI5ba125N+xnZTUEJ3OC52utKnMTb/ujUbB1avN8x0X6PWSiAhvPDyiOXnSBx+fk5w82RGj0eEu4wlatLhcaHoRjUZDly5d8PX1NU+fmc5tJDIykj59+iiRqAJEpkQyfv14DkQdYKzPWBYPX4xLbRdbm6Wo5JRKLKSUezGtekJKmYhphdPtba4Cw/K9zgAalcfI6kr+Hdv33nuCtLR67No1qBwj3h6w1uDklIHRCPHxrhw/7lfsCFJqCAvzxcPjCkIYMRo1JCQ0okmTeIQQ+Pr6mgVBo9FgMBjQaDRq2qkKIKVkeehypm2bhhCCVY+u4pnOz5R4MYaiZqOKH9mQpKSkAq979TpE48Zx3FrGWhry/4e/JRg3bjgRFdWCEyfuLfFITk4ZCCExhSQkp075oNN5mD0IRdUj5noMI9eO5Lk/nqOLexfCXgxj3L3jlFAoSowSCxuh0+lISEgwvxbC9Hf//Ucom1iYRyL/UtjExEasX/9IbmC7ZERGemMwaDEYBEajlogIbyIjvZFS4uTkhE6nMy+hhVt7LBSVDykla06swedrH3Ze2skXQ75gz8Q9pa6volCo3FA2IjIyssBeBSkhPd2J7dsDuNtqpdKh4fhx31KPFx3tyYoVE/D2jiQysgUxMc0YPHgnAMHBwcTGxt6Rbyo0NLTAFJXC9sTfiOflLS/zy+lf6OHRg+WjlquUHYoyo8TCRhQ2x3/9en0MBi2WEYu7rXoqnuhoz9wUIEa6dQsy7+a+evVqoe3zpqiUWFQO1p9ZzwubXiA1K5VPBn7CGw+8gZ1G/XdXlB1199gQjUaTuyHPtJopLKxz7oa5yoEQEq3WgK/v8Tve0+k8uHy5JS1bXsbDQ2feW6ESC9qW5MxkXtv2Gj8c/4EuTbuw+9HddHLrZGuzFNUAJRY24vZpqODgrhw50tNi42s08MYb8PnnFEgDkj9RYd5rU1GlwkaRNG9+xbzRDkweRHS0J6tWTcBg0HDggOSzz/bSqlUGcXFxKrGgjZBSsvn8Zl7Y9AJxN+KY1XcW/+rzL1WcSGExVIDbgqTcTOF61nWcazkX29bb29u8A9pg0BAWdi+3gtNlRwiTUDz1FDRoAP/8J9jbm47Vrg3ffAMvvggODqDVmv4K21MBpmW0ly+3YNmySdjZvczEiRPp378/DRs+ag6Au7ldITn5MMHBwWzevBm9Xl8g5YfC+gRdDeKhVQ/x8JqHaVirIUcmH+H9fu8roVBYFOVZWJAdF3dgkAaGtB5Sqn4ajaRhw6TcGEHZxCJPINatM3kSq1ebjjk6wn//C4mJ0K8f9Mx1XiZMgL17oVEjeO01yMoqamTTru5//7sBzs5ReHt7k5bWADs7k8iYst3emVgQwMnJqUyfRVEyLiVf4t3d77L25FoaOzXmy4AvecH/BRztHG1tmqIaosSijARdDeKQ7pD5tRCCX07/gkttlxKVUs2/A1oIyaOPbiAry5Fz5+7JG7HUNsXH3561FrKzTUIx47YUjj173hKOzp1h+nQ4erTosfV6ya+/JtKjx2a2bJmAXu+JlBAR4U1OjhZ7e32B9lJKtm3bhpubm5qKsjDxN+KZvW82i48txk5jx7t93uWtXm+pnE4Kq6LEooRk5GSwO2I3OYYc4jPieW3ra2QZ7vw5PrXr1BKVncy/GipvX1TbthfyiUXpsLeH0aNh/36Tl2A0mjwLBweTR3E3evaEhQth4MBbfQtixM7OVDNDozHQoEEiUVEmAZASwsJ88fXNplat0wWSDarss5Yl5noMCwMXsvjYYm7k3OD5Ls8zq98smtVrZmvTFDWAGisWZxPO8vr21zEYDfTw6EFPj5708OhBw9oN72h7Ku4UY38Zy+n40+ZjPq4+bH56M3Uc6gCmX9ISSWOnxmWyRwiIicn7T186r0IIePZZmDrV5CXkTS/dPvV0N/IE45VXCoqFvT2MHXuDOnV+pVmzK0REeBEeblqr7+GhY+LElWi1BuzstPTo0YOIiAiuXbuGlFJln7UQF5IuMO/gPJaHLUdv1DOm4xje7/s+HVw72No0RQ2iRopFjiGHMT+PISI5gjYubZizfw5GafqG7NC4Az09elLfsT47Lu2gZYOW7I7YTX3H+vw29jdaNWwFQPvG7allV6vMNhS2Kc/PL5SQkC6500glFwyNxhSDgILTS6UlMfHOYLcQ8Mor9fDwGMimTYl8/PG9ZGebAvMtW0ai1RrQaCRSGjh8+DBSygIJB5VXUXaCY4L57OBn/HL6F+w19jzn9xxvPPAGrV1a29o0RQ2kRorFl0e+5GTcSTY8uYGR7UeSnp3O0StHOaw7zOHow2wI30BaVhoPtniQkGsh9PPux7JRy2ha9/Ya2GXH29sbrVZbYNrGyyuadu3OEh5eul+MDz9cdoHIT79+pmmrmzdviYZeD7NmwaxZnqSkeJqX4Wo04OrqjZ2dFikNCCEKZKF1djatCNu/f7/ac1EKMnIy+OX0LywNXsr+qP3Ud6zPWw+8xbQe0yx6/ykUpUUUtorF1nTr1k0eO3bMKmNn5GTQ7PNm9PLqxeanNxfaRkpJtiHb6qtKdDodhw4d4uzZs+YvZ53Og2XLnqU0q5r/9z/TFJQlOHwYVq6EZctMQpEX+3B0NE1TTZ9uCpo7OMCuXeDgEMTp06dxd3fnyJEj5j0WAQEBas9FKQiJCWFp8FJWn1hNalYqbV3aMqXrFKb6Ty3RUmyFQggRJKXsZq3xa5xn8d+j/yU1K5V/9f5XkW2EEBWy/NDT05MnnniCdevWcebMWYSA2Fg3ShuzCAmxnE1501gTJpg8ip07TYLh6qojLi6Sr75yJTDQkaFDnfDwyGblSpMgXL58mbZt2yKlpG7dusTExJhrdatAd+Hc1N9k3cl1fH3sa45eOUotu1o83vFxJneZzIMtHlQZYRWViholFnqjno/3f8zQNkPp5dXL1uaYadOmjdm7uHSpVb538lKNVzw9e5rEYv9+k1CMH78Sg0FPVBQ0by44dUqLnZ1vAUE4e/YsYBJbjUZjTmeiAt0FiUiOYPGxxSwLWUZiZiL3NL6HRQGLGHfvuEIXWCgUlYEaJRapN1NJzUplUKvyFBiyPBkZGYApmNyjxxHOn2+HwWCqlV1crqj8we3yUlhep4kToU6dSOztb8VW8sQBTPW39fo791gYjUbatWtHTk4OHTt2rPFehVEa2XZhG1///TVbzm9BIzQ8cs8jvHzfy/T37q+8CEWlp0aJRWRKJECly+Xv7e2NEAIpJV5epuWoCQku2NvnEBjYM3dnd/7YkumLRaOBxYstE9zW6XSsXLnSHGPw8ZnM0083ITvbZN/EiVqMRpMo5OWK8vX1xdfXl7CwMEJDQ80CkudZXLhwAaPRSFRUVJXYnJeZk8nak2tZEbYCL2cvJvlNop93PzSi7FlxbupvsjJsJfMOzeNC0gWa1GnCzAdnMtV/Kh71PSxovUJhXWqUWJTnP7018fT0ZPjw4eZ63B4e0Xh4RCMltG9/nhUrxucKBoDE1zeDnj3rMGGCZYQCTEt5808pbd2aQXa2KXVIZKQnGs0E+vWLxMnJiYyMjALeh6enp7kud977qampBAcHV5mYRdi1MEb/NJqLyRdp36g9oddCWXV8FR1dO7L/2f2lrlGdlpXGN8e+4YvAL7iWfo37mt3HmtFreKzDYzho71YDXaGonNQoscibD06+mWxjS+7E39+fI0eOcPBgfZo1i6FOnQw0GpDSgLf3ZaKjPalfP5Xr1+vj6xvF4sWW3ZCVt5RXr9cjhKBHjyxWrbq18mnAAE969iz6y97T07OAGOh0OsLCwsyeSmWOWSwPXc5Lm1/CpbYL257ZxuDWg7mpv8lPp37i+T+e591d77J4xOISjRWbHsuXR77k67+/Nk95rn5stZpqUlR5apRY5G28y3usbLi4uODsnETt2qYYhinPk4bLl72ws9Pz6KO/s2dPfxwdA9Hp6lrkl/rhw6Yd3/36eRIQEMCWLVuQUnL16q/8+ONkzpxpUuJd4Pnx9PRkwoQJlbq2xZn4M8zYNYMN4RsY0HIAa0avwa2OGwC17Wsz0W8iIddCWHRkEc91eY77mt9XoH/KzRQikiOITovmWvo1gmKCWBG2gix9Fo93fJy3e72NfzN/W3w0hcLi1CixOJtgWq3T0bWjjS0pnF69ehEevgwpb9WdyMqyp127CwwevJPmzaPp3v0oHh46i0zrHD5sygeVnW1KVR4Q4I6bW3M8PHQYDAYaNTrHjBlNyjz+7d5GZUGXqmP2vtksDVlKHfs6fDLwE9584M1Cc3p90O8D1p1ax3N/PMfAlgOJTIk0/6VmpRZo66B1YKLvRN584E3aNmpbUR9HoagQapRYpN40/ecu7fxzReHp6UmLFi24fPkyYBIMJ6eb9OlzILeanoajR7vj4nLDItM6e/dijksYDLBxozsazQS6dAmla9eTlXrqqCzoUnV8+NeHLA9bjkDwj/v+wcwHZ+Jax7XIPs61nPky4Eue+OUJIpIj8G7gjXcDb/p49TE/93T2xL2uO03qNlHxCEW1pcRiIYTQAseAK1LKEUIIF2Ad4A1EAmOllHcEA4QQDYClQCdMS3qek1IeLrflZSAtKw2Aug51rXYOg9FAt2+7MeHeCbze8/VS9x84cCDff/+9OW9UnodhNGrYvHkY0dFeNGjwCJ6eDUo0XlFlTg8fhqgosLMzbbozTXkJDAYtx475c/JkVyZN0uDpWfQYVYW4G3F8vP9jFh8zxR2mdp3KW73eokWDFiXqP9ZnLMPbDsfJ3knFHRQ1ltJ4FtOAM0Be0vx3gF1Syk+FEO/kvn67kH5fAtuklI8LIRwAm1XEycvSufX8Vqb4T7HKOY5eOUrotVBCr4WWSSw8PT159tln2bVrl9nDSE934pdfnkCn88LRER55pEGJxrp9OWxeyo3800+3vvsKLs3Nzpb8/nsKHh7XCx2jKpByM4X5h+azMHAhmfpMJvlO4r2+75VYJPKTl11YoaiplGgtqRDCAxiOyUPIYxSwIvf5CuCRQvrVBx4EvgOQUmZLKVPKbm756OPVB393f776+yurnWND+Abz87Lm3fL09GTgwIHmsqvOzjdZvFgwe7YpH1NJg823L4fNK3Nqmn6SuLvr8PCIyLXzVklXIQxoNAZSUn43r2iqKqVSjdLIrku7mLB+As0XNGfO/jmMaDeC0y+f5rtR35VJKBQKRck9i4XAW0C9fMeaSCljAKSUMUIIt0L6tQLige+FEL5AEDBNSnnj9oZCiKnAVAAvL68Sf4DSIISgjUsbDuoOWmV8gD/C/zA/j8+IN6+uKS35U5ib8i1FMmNG6X7R589sm3/5qpdXGGPGnKZt2/PExbmydetQvLx0REa2oHHjRFxckmjZMpLmza8AjQsdoyhsNWV1LvEcK0JXsOr4KnRpOuo71ueZzs/w8n0v49fUr8LsUCiqK8WKhRBiBBAnpQwSQvQrw/hdgVellEeEEF9imq769+0NpZRLgCVgyjpbyvOUiPTsdDad28STnZ60xvCcTzzPmYQzDGk9hO0Xt3Mp+VKZxaKoL/rSUNjyVZ1Ox8WLG2jf3nSJ3d3jmDTJ5CBKqSE1tT4uLikAaLV25l3aRQlAfnGIi4szL72tiCmrlJsprDu5juVhywmMDkQjNAxuPZi5g+Yyqv0oatvXttq5FYqaRkk8i17ASCHEMKAWUF8I8QMQK4Rwz/Uq3IG4QvpGA9FSyiO5r3/BJBY24ZfTv3Aj5waT/CZZZfw8r2J6j+lmsShJPe7CsNQ+hduXr+Z5LPnjtLmzXUhppGHDFITQ0LVrF9zd3c3n79Onzx1j54+J5CUNzPOGrLVrW2/Us+PiDlaErWDD2Q1kGbLwcfVh7kNzeebeZ1SJUYXCShQrFlLKGcAMgFzP4g0p5TghxDxgIvBp7uOGQvpeE0LohBDtpZThwEDg9O3tKorVJ1bTxqUNvTwtn3HWYDSwPGw5vk186duiLwCXki+Va0xr7FO4veiSEAK9NCKNIKSGc+fa0blzL3x9KTawfXtMJD9CCIsuvT0Re4IVYStYfWI119Kv0ah2I6b6T2Wi70S6undVq5QUCitTnn0WnwI/CSGeB6KAMQBCiGbAUinlsNx2rwKrc1dCXQKeLcc5y8XZhLMMbDnQKl8s/wv6HyfjTvLzmJ+pbV8b97ru5RYLa5DnsWz/eztBqUGcl+c5dDUQQ7YDdYJf4+ZfTzBtmiAycn+x9SicnJwKBPGjoz1JSGhIq1ZRPP1073ILXfyNeH488SMrwlYQci0EO40dI9qNYKLvRIa1Hab2NCgUFUipxEJKuRfYm/s8EZOncHubq8CwfK9DAatVbyopRmnkWvo1q0xTJGQkMHP3TAa0HMDoDqMBaNWwVaUTizPxZ5h7aC6bz20mPiMegNYNW/Nc10nsvxDKiR4f8smLXThbK5mwqDACCcSAgYaiIY2MjeAyONk7Udu+NrXtapOQlmAeW6fzYNWqiej1GhwcJI8/XvakjVevX2XW3ll8H/o9eqMef3d/FgUs4qnOT9HYqXG5r4NCoSg9NWYHd/yNePRGPe513S0+9szdM0nLSmNRwCKz19KqYSv2Ru61+LnKQt6X73ch31HbrjaPdXiM+5vfz+DWg81pKX44/gPj1x9iRuijEAoCQX2H+iAhNSeVP/f+ece4WqGlGc3wxpsbqaPQiyyMRidycgR795Y+n1RaVhrzDs5jQeACcgw5vOD/Ai91ewkfN5/yXwSFQlEuaoxYXEy+CEBrl9YWHTckJoQlQUt47f7XCnypeTl7EZ0WnRtMts18elpWGnMPzmXB4QXojXpe7f4qMx+cWeiv8xHtRvDFkC/wcvaik1snWjVshZ3GdHukZ6cTmRJJzPUYMvWZZOZkkqnP5ELSBbaFb+NQ/CEMnfbDPf+CmG4Ya6XxifEKX8yzw9HOEQetAw5aBxy1+Z7fdtxea8+fF/8kPiOeJ3yeYM6AORb/t1IoFGWnxojFucRzALRr1M5iY0opeXXrqzR2asysfrMKvGeURrQarU2EwiiNfBv0LTP3zCQhI4EnOz3JnAFzaNWwVZF9GtRqwPQe0wt9r65DXTq5daKTW6c73ps9YDbXs65zIOoAPxzczUH7w3g2bsm9LR7EIA1kG7LJNmSTZci69VyfRZY+i+tZ1wsc7+relY/6f3RHdleFQmF7aoxYnE88j53GzqJV8lafWM1B3UG+G/kdDWo1KPBeZk4mte3Kvs4/LSuN7Re2417Pnd5evUvcLzIlknG/jeOg7iB9W/Rl/uD5dGtm3ZBRPcd6DG07lKFth1r1PAqFwnbUGLE4l3SuwNRKebmedZ23/nyL+5rdV+i+jZv6m6XeFBaVGsXG8I38ce4P9kTsIceYQ4fGHTj9SslWG284u4FJGyZhlEaWj1rOBN8JakmpQqGwCDVHLBLPWXQKava+2cSkx7D+ifWFlmvN1GdSy67WXceQUnIm4Qy/nfmN9WfXExwTDIBHfQ9yjDkA9PPuV6wtOYYc3tn5DgsCF9DVvSs/j/n5rlNOCoVCUVqqtVj8eOJHdl7ayeDWgzmXeI5BrQZZZNzwhHC+CPyCZ/2e5X6P+wttk6kvfBpKSsnfV/9m/Zn1/Hb2N3MspYdHDz576DNGtR/FgsMLWBqylA/7fci0HtPuaktUahRP/PIEgdGBvHLfK3w++HMc7RzL/yEVCoUiH9VWLP6K/IuJv09ESsn3od8DMMF3QrnHlVIyfft0atvX5pOBnxTZLjMn0zwNpTfq2Xd5H+vPrOf38N+JTotGK7T0b9mfafdPY1T7UTSv39zcNzQ2lAEtB/Dug+8WOX5UahQ/nfqJTw58Qo4hh3WPr2Osz9hyfz6FQqEojGopFlGpUYz5eQxtXNpw4NkDhFwLIT07nXub3FvusTee28i2C9tYMHgBTeoWXXI0LSuN0GuhPLvhWTaGbyQxM5FadrUIaBPAnAGmtNlFVexLzkymZYOWdxzXper45fQv/HT6JwKjAwHo7dWbZSOXqTKeCoXCqlQ7scg2ZPPI2kfIMmTx+xO/08ipEQ+1esgiY9/U3+T17a/ToXEH/tH9H3dtmxdIX39mPSPajeCxDo8xpPWQEhXRSb6ZTMNaDc2v90buZebumebU6l2aduGTgZ8wpuMYtRdBoVBUCNVOLA7pDhFyLYRVj66ifeP2Fh3780Ofcyn5En+O/xN7rf1d23417Ct0aToebPFgqXIYSSlJzkymYe2GpGWl8fafb/NN0De0bNCS2f1nM8ZnjEUD9QqFQlESqp1Y5K0oGtx6sEXHjUiO4OMDH/NYh8dK5Km0b9y+TGKVnp2OQRr4++rfdPq6E1euX+GfPf/Jh/0/xMneZhVpFQpFDafaicX5xPO41HYpc9GhwjBKI5M2TEIrtHwx5AuLjVsY17OvA7Dz0k7uaXwPB587WOaaGAqFQmEpqp1Y5E3fGKWx0P0PZWHRkUXsu7yPZSOX4eVsnZKveTSt25SP+n+EvcaeaT2mFbtXQ6FQKCqCaicW7nXd0Rv1JGQkWMS7OJtwlhm7ZjCi3QirVdjLj0ZomPngTKufR6FQKEqDZX56VyLc65lSkMdcjyn3WHqjnom/T8TJ3oklI5ao1BkKhaLGUu3EIq+4UUx6+cVi7sG5HL1ylMXDF5tFSKFQKGoi1U4s8oobldez+OH4D8zaO4snfJ5QO6MVCkWNp/qJRd40VBk9i8ycTKZunMr49ePp4dGDr4d/bUnzFAqFokpS7QLc2y9sB6CeQ71S972QdIExP48h9FooM3rP4MP+H1ospblCoVBUZarVN2FmTiZTNk6hS9MuvNDthVL1/eX0Lzy34TnsNHZsemoTw9sNt5KVCoVCUfWoVmKx7cI24jPiWf3Y6hKn2Mg2ZPPmjjdZdHQR9ze/n3WPr6NFgxZWtlShUCiqFtVKLH4+/TONnRrTv2X/ErW/nHKZsb+M5eiVo0y/fzqfDfqsVHmcFAqFoqZQYrEQQmiBY8AVKeUIIYQLsA7wBiKBsVLK5EL6RQLXAQOgl1JarSB0YHQgA1sOLFGcYdO5TUxYPwGDNPDr2F95rMNj1jJLoVAoqjylWQ01DTiT7/U7wC4pZVtgV+7rougvpfSzplAAZORk4OzofNc2OYYc3v7zbR5e8zDeDbwJnhqshEKhUCiKoURiIYTwAIYDS/MdHgWsyH2+AnjEopaVgZv6m3edRjoTf4YBKwcw99BcXvR/kUPPH1L1IBQKhaIElHQaaiHwFpB/PWoTKWUMgJQyRghRVCImCewQQkjgf1LKJYU1EkJMBaYCeHmVLVlf+8btORZzrMCx8IRwfj79Mz+d+okTcSeoY1+H1Y+t5unOT5fpHAqFQlETKVYshBAjgDgpZZAQol8ZztFLSnk1V0z+FEKclVLuu71RrogsAejWrZssw3kY2mYoH/71IYHRgey6tIufTv/E8djjJiM8e/FlwJeM6ThGpe5QKBSKUlISz6IXMFIIMQyoBdQXQvwAxAoh3HO9CncgrrDOUsqruY9xQoj1QHfgDrGwBEPbDOWDvz6g53c9TYZ79mLhkIWM7jgaj/oe1jilQqFQ1AiKFQsp5QxgBkCuZ/GGlHKcEGIeMBH4NPdxw+19hRB1AI2U8nru88HAhxaz/jbua34f7/R6hyZ1m/B4x8eVQCgUCoWFKM8+i0+Bn4QQzwNRwBgAIUQzYKmUchjQBFifm9rbDvhRSrmtfCYXjUZo+OShT6w1vEKhUNRYSiUWUsq9wN7c54nAwELaXAWG5T6/BPiW10iFQqFQ2JZql3VWoVAoFJZHiYVCoVAoikWJhUKhUCiKRYmFQqFQKIpFiYVCoVAoikWJhUKhUCiKRYmFQqFQKIpFSFmmNExWRQgRD1wuxxCNgQQLmWMNKrN9ldk2qNz2VWbboHLbV5ltg6phXx0ppau1TlApxaK8CCGOWbt2RnmozPZVZtugcttXmW2Dym1fZbYNlH2gpqEUCoVCUQKUWCgUCoWiWKqrWBRaYKkSUZntq8y2QeW2rzLbBpXbvspsGyj7qmfMQqFQKBSWpbp6FgqFQqGwIEosFAqFQlEsVUoshBDrhBChuX+RQojQ2973EkKkCyHeKKL/PCHEWSHEcSHEeiFEg9zj3kKIzHxjf2MD21yEEH8KIc7nPjbM994MIcQFIUS4EGJIaW27m31CiO75jocJIR4tZX+rXbtS2DZLCHElX9th+d6rDNeuwu+7Uthmq/tukBAiSAhxIvdxQCn7l/vaWcg+q917FrDNsvedlLJK/gGfA+/dduxX4GdMpV8L6zMYsMt9/hnwWe5zb+CkjW2bC7yT+/ydfLZ1BMIAR6AlcBHQWso+wCnfNcmrpW5Xiv5Wu3YltQ2YVdh1rSzXzhb3XSlss9V91wVolvu8E3CllP0teu3Kal9F3XtltM2i912V8izyEEIIYCywJt+xR4BLwKmi+kkpd0gp9bkvAwGLF+kuq23AKGBF7vMVwCP5jq+VUmZJKSOAC0B3S9knpczId01qAXdd8VDY57MU5bWtECrFtbPFfVeKa2er+y5Emqpqgun/RS0hhGNJ+1ua8tpXCBa7fmW1zdL3XZUUC6APECulPA8ghKgDvA18UIoxngO25nvdUggRIoT4SwjRxwa2NZFSxgDkPrrlHm8O6PK1i849ZhH7cm28XwhxCjgBvJjvBitRf6x07Upp2z9y3e1l+aZSKtu1gwq670phm83uu3yMBkKklFml7G+pa1de+6x975X32oEF7rtS1eCuCIQQO4Gmhbz1rpRyQ+7zpyj4C+MD4AspZbpJhIs9x7uAHlideygG8JJSJgoh/IHfhRA+Usq0iratMHMLOVbor8Qy2oeU8gjgI4ToAKwQQmyVUt4swp7b+1vz2pXUtsXAR5iuy0eYXPbnqGTXroLvu9L+u95hbiHHLHrtcvv6YJoiGVyMPWW67yrAvnLdexVx7cp6391pvQXn/CriD5PAxQIe+Y7tByJz/1KAJOAfRfSfCBwGnO5yjr1At4q0DQgH3HOfuwPhuc9nADPytdsO9LTUtSukzZ6iPnsJ+1vs2pXGtnxtvMmdj61k165C77uS2mbL+w7TtMg5oJcFPl+Zrp0l7LPmvWeBa2ex+67UF9bWf0AA8Ndd3p9F0UHkAOA04HrbcVdyg09AK+AK4FLBts2jYKBxbu5zHwoGyi5RxkBZYfbljpkXBGsBXAUal6K/1a5dSW0j98su9/nrmOaKK821s8V9VwrbbHXfNcgdf3QZ+1vk2pXXPmvfe+W0zaL3XakvrK3/gOWY5l+Len8W+b6QgaXkqiamIJMOCM39+yb3+GhMgaIwIBh42Aa2NQJ2AedzH13ytXsX02qKcGCoJa8dMD73s4fmfvZHCrPvLv2tdu1KahuwCtO8/HHgj9v+A9v82tniviuFbba672YCN/Jdk1DAraLvu/LaZ+17r5y2WfS+U+k+FAqFQlEsVXU1lEKhUCgqECUWCoVCoSgWJRYKhUKhKBYlFgqFQqEoFiUWCoVCoSgWJRYKhUKhKBYlFgqFQqEolv8HWsj+Thou2+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  776873.625\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.302 1.797\n",
      "fractional expected GOP seats =  1.955  out of  16 seats for NYC-LI\n",
      "simpler expected GOP seats =  2.0531  out of  16 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOTE - I DID NOT PULL MEMPHIS BACK INTO THIS ANALYSIS. (can be pieced together if needed)\n",
    "#I manually corrected responsiveness and stateGOP2 for the added Dem district"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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